An adaptive state of charge estimation approach for lithium-ion series-connected battery system

NASA Astrophysics Data System (ADS)

Peng, Simin; Zhu, Xuelai; Xing, Yinjiao; Shi, Hongbing; Cai, Xu; Pecht, Michael

2018-07-01

Due to the incorrect or unknown noise statistics of a battery system and its cell-to-cell variations, state of charge (SOC) estimation of a lithium-ion series-connected battery system is usually inaccurate or even divergent using model-based methods, such as extended Kalman filter (EKF) and unscented Kalman filter (UKF). To resolve this problem, an adaptive unscented Kalman filter (AUKF) based on a noise statistics estimator and a model parameter regulator is developed to accurately estimate the SOC of a series-connected battery system. An equivalent circuit model is first built based on the model parameter regulator that illustrates the influence of cell-to-cell variation on the battery system. A noise statistics estimator is then used to attain adaptively the estimated noise statistics for the AUKF when its prior noise statistics are not accurate or exactly Gaussian. The accuracy and effectiveness of the SOC estimation method is validated by comparing the developed AUKF and UKF when model and measurement statistics noises are inaccurate, respectively. Compared with the UKF and EKF, the developed method shows the highest SOC estimation accuracy.

Built environment and Property Crime in Seattle, 1998-2000: A Bayesian Analysis.

PubMed

Matthews, Stephen A; Yang, Tse-Chuan; Hayslett-McCall, Karen L; Ruback, R Barry

2010-06-01

The past decade has seen a rapid growth in the use of a spatial perspective in studies of crime. In part this growth has been driven by the availability of georeferenced data, and the tools to analyze and visualize them: geographic information systems (GIS), spatial analysis, and spatial statistics. In this paper we use exploratory spatial data analysis (ESDA) tools and Bayesian models to help better understand the spatial patterning and predictors of property crime in Seattle, Washington for 1998-2000, including a focus on built environment variables. We present results for aggregate property crime data as well as models for specific property crime types: residential burglary, nonresidential burglary, theft, auto theft, and arson. ESDA confirms the presence of spatial clustering of property crime and we seek to explain these patterns using spatial Poisson models implemented in WinBUGS. Our results indicate that built environment variables were significant predictors of property crime, especially the presence of a highway on auto theft and burglary.

Built environment and Property Crime in Seattle, 1998–2000: A Bayesian Analysis

PubMed Central

Matthews, Stephen A.; Yang, Tse-chuan; Hayslett-McCall, Karen L.; Ruback, R. Barry

2014-01-01

The past decade has seen a rapid growth in the use of a spatial perspective in studies of crime. In part this growth has been driven by the availability of georeferenced data, and the tools to analyze and visualize them: geographic information systems (GIS), spatial analysis, and spatial statistics. In this paper we use exploratory spatial data analysis (ESDA) tools and Bayesian models to help better understand the spatial patterning and predictors of property crime in Seattle, Washington for 1998–2000, including a focus on built environment variables. We present results for aggregate property crime data as well as models for specific property crime types: residential burglary, nonresidential burglary, theft, auto theft, and arson. ESDA confirms the presence of spatial clustering of property crime and we seek to explain these patterns using spatial Poisson models implemented in WinBUGS. Our results indicate that built environment variables were significant predictors of property crime, especially the presence of a highway on auto theft and burglary. PMID:24737924

On the Use of Statistics in Design and the Implications for Deterministic Computer Experiments

NASA Technical Reports Server (NTRS)

Simpson, Timothy W.; Peplinski, Jesse; Koch, Patrick N.; Allen, Janet K.

1997-01-01

Perhaps the most prevalent use of statistics in engineering design is through Taguchi's parameter and robust design -- using orthogonal arrays to compute signal-to-noise ratios in a process of design improvement. In our view, however, there is an equally exciting use of statistics in design that could become just as prevalent: it is the concept of metamodeling whereby statistical models are built to approximate detailed computer analysis codes. Although computers continue to get faster, analysis codes always seem to keep pace so that their computational time remains non-trivial. Through metamodeling, approximations of these codes are built that are orders of magnitude cheaper to run. These metamodels can then be linked to optimization routines for fast analysis, or they can serve as a bridge for integrating analysis codes across different domains. In this paper we first review metamodeling techniques that encompass design of experiments, response surface methodology, Taguchi methods, neural networks, inductive learning, and kriging. We discuss their existing applications in engineering design and then address the dangers of applying traditional statistical techniques to approximate deterministic computer analysis codes. We conclude with recommendations for the appropriate use of metamodeling techniques in given situations and how common pitfalls can be avoided.

Statistical analysis for understanding and predicting battery degradations in real-life electric vehicle use

NASA Astrophysics Data System (ADS)

Barré, Anthony; Suard, Frédéric; Gérard, Mathias; Montaru, Maxime; Riu, Delphine

2014-01-01

This paper describes the statistical analysis of recorded data parameters of electrical battery ageing during electric vehicle use. These data permit traditional battery ageing investigation based on the evolution of the capacity fade and resistance raise. The measured variables are examined in order to explain the correlation between battery ageing and operating conditions during experiments. Such study enables us to identify the main ageing factors. Then, detailed statistical dependency explorations present the responsible factors on battery ageing phenomena. Predictive battery ageing models are built from this approach. Thereby results demonstrate and quantify a relationship between variables and battery ageing global observations, and also allow accurate battery ageing diagnosis through predictive models.

The Dynamics of Population, Built-up Areas and their Evolving Associations in Gridded Population across Time and Space

NASA Astrophysics Data System (ADS)

Stevens, F. R.; Gaughan, A. E.; Tatem, A. J.; Linard, C.; Sorichetta, A.; Nieves, J. J.; Reed, P.

2017-12-01

Gridded population data is commonly used to understand the `now' of hazard risk and mitigation management, health and disease modelling, and global change-, economic-, environmental-, and sustainability-related research. But to understand how human population change at local to global scales influences and is influenced by environmental changes requires novel ways of treating data and statistically describing associations of measured population counts with associated covariates. One of the most critical components in such gridded estimates is the relationship between built-up areas and population located in these areas. This relationship is rarely static and accurately estimating changes in built-areas through time and the changing human population around them is critical when applying gridded population datasets in studies of other environmental change. The research presented here discusses these issues in the context of multitemporal, gridded population data, using new technologies and sources of remotely-sensed and modeled built-up areas. We discuss applications of these data in environmental analyses and intercomparisons with other such data across scales.

Evaluation of "e-rater"® for the "Praxis I"®Writing Test. Research Report. ETS RR-15-03

ERIC Educational Resources Information Center

Ramineni, Chaitanya; Trapani, Catherine S.; Williamson, David M.

2015-01-01

Automated scoring models were trained and evaluated for the essay task in the "Praxis I"® writing test. Prompt-specific and generic "e-rater"® scoring models were built, and evaluation statistics, such as quadratic weighted kappa, Pearson correlation, and standardized differences in mean scores, were examined to evaluate the…

Within tree variation of lignin, extractives, and microfibril angle coupled with the theoretical and near infrared modeling of microfibril angle

Treesearch

Brian K. Via; chi L. So; Leslie H. Groom; Todd F. Shupe; michael Stine; Jan Wikaira

2007-01-01

A theoretical model was built predicting the relationship between microfibril angle and lignin content at the Angstrom (A) level. Both theoretical and statistical examination of experimental data supports a square root transformation of lignin to predict microfibril angle. The experimental material used came from 10 longleaf pine (Pinus palustris)...

Metrological traceability in education: A practical online system for measuring and managing middle school mathematics instruction

NASA Astrophysics Data System (ADS)

Torres Irribarra, D.; Freund, R.; Fisher, W.; Wilson, M.

2015-02-01

Computer-based, online assessments modelled, designed, and evaluated for adaptively administered invariant measurement are uniquely suited to defining and maintaining traceability to standardized units in education. An assessment of this kind is embedded in the Assessing Data Modeling and Statistical Reasoning (ADM) middle school mathematics curriculum. Diagnostic information about middle school students' learning of statistics and modeling is provided via computer-based formative assessments for seven constructs that comprise a learning progression for statistics and modeling from late elementary through the middle school grades. The seven constructs are: Data Display, Meta-Representational Competence, Conceptions of Statistics, Chance, Modeling Variability, Theory of Measurement, and Informal Inference. The end product is a web-delivered system built with Ruby on Rails for use by curriculum development teams working with classroom teachers in designing, developing, and delivering formative assessments. The online accessible system allows teachers to accurately diagnose students' unique comprehension and learning needs in a common language of real-time assessment, logging, analysis, feedback, and reporting.

Micro-foundations for macroeconomics: New set-up based on statistical physics

NASA Astrophysics Data System (ADS)

Yoshikawa, Hiroshi

2016-12-01

Modern macroeconomics is built on "micro foundations." Namely, optimization of micro agent such as consumer and firm is explicitly analyzed in model. Toward this goal, standard model presumes "the representative" consumer/firm, and analyzes its behavior in detail. However, the macroeconomy consists of 107 consumers and 106 firms. For the purpose of analyzing such macro system, it is meaningless to pursue the micro behavior in detail. In this respect, there is no essential difference between economics and physics. The method of statistical physics can be usefully applied to the macroeconomy, and provides Keynesian economics with correct micro-foundations.

Higher Education Enrollment: Projections 2015-2023

ERIC Educational Resources Information Center

Von Nessen, Erica M.

2015-01-01

This report provides an overview of enrollment trends and enrollment projections at both the undergraduate and graduate level, by sector, for public colleges and universities in South Carolina. Using institutional enrollment data from the late 1970s through 2014, statistical models were built for each sector to determine which factors influence…

The spatial clustering of obesity: does the built environment matter?

PubMed

Huang, R; Moudon, A V; Cook, A J; Drewnowski, A

2015-12-01

Obesity rates in the USA show distinct geographical patterns. The present study used spatial cluster detection methods and individual-level data to locate obesity clusters and to analyse them in relation to the neighbourhood built environment. The 2008-2009 Seattle Obesity Study provided data on the self-reported height, weight, and sociodemographic characteristics of 1602 King County adults. Home addresses were geocoded. Clusters of high or low body mass index were identified using Anselin's Local Moran's I and a spatial scan statistic with regression models that searched for unmeasured neighbourhood-level factors from residuals, adjusting for measured individual-level covariates. Spatially continuous values of objectively measured features of the local neighbourhood built environment (SmartMaps) were constructed for seven variables obtained from tax rolls and commercial databases. Both the Local Moran's I and a spatial scan statistic identified similar spatial concentrations of obesity. High and low obesity clusters were attenuated after adjusting for age, gender, race, education and income, and they disappeared once neighbourhood residential property values and residential density were included in the model. Using individual-level data to detect obesity clusters with two cluster detection methods, the present study showed that the spatial concentration of obesity was wholly explained by neighbourhood composition and socioeconomic characteristics. These characteristics may serve to more precisely locate obesity prevention and intervention programmes. © 2014 The British Dietetic Association Ltd.

Statistical Learning Theory for High Dimensional Prediction: Application to Criterion-Keyed Scale Development

PubMed Central

Chapman, Benjamin P.; Weiss, Alexander; Duberstein, Paul

2016-01-01

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in “big data” problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how three common SLT algorithms–Supervised Principal Components, Regularization, and Boosting—can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach—or perhaps because of them–SLT methods may hold value as a statistically rigorous approach to exploratory regression. PMID:27454257

Liquid water breakthrough location distances on a gas diffusion layer of polymer electrolyte membrane fuel cells

NASA Astrophysics Data System (ADS)

Yu, Junliang; Froning, Dieter; Reimer, Uwe; Lehnert, Werner

2018-06-01

The lattice Boltzmann method is adopted to simulate the three dimensional dynamic process of liquid water breaking through the gas diffusion layer (GDL) in the polymer electrolyte membrane fuel cell. 22 micro-structures of Toray GDL are built based on a stochastic geometry model. It is found that more than one breakthrough locations are formed randomly on the GDL surface. Breakthrough location distance (BLD) are analyzed statistically in two ways. The distribution is evaluated statistically by the Lilliefors test. It is concluded that the BLD can be described by the normal distribution with certain statistic characteristics. Information of the shortest neighbor breakthrough location distance can be the input modeling setups on the cell-scale simulations in the field of fuel cell simulation.

A Weibull statistics-based lignocellulose saccharification model and a built-in parameter accurately predict lignocellulose hydrolysis performance.

PubMed

Wang, Mingyu; Han, Lijuan; Liu, Shasha; Zhao, Xuebing; Yang, Jinghua; Loh, Soh Kheang; Sun, Xiaomin; Zhang, Chenxi; Fang, Xu

2015-09-01

Renewable energy from lignocellulosic biomass has been deemed an alternative to depleting fossil fuels. In order to improve this technology, we aim to develop robust mathematical models for the enzymatic lignocellulose degradation process. By analyzing 96 groups of previously published and newly obtained lignocellulose saccharification results and fitting them to Weibull distribution, we discovered Weibull statistics can accurately predict lignocellulose saccharification data, regardless of the type of substrates, enzymes and saccharification conditions. A mathematical model for enzymatic lignocellulose degradation was subsequently constructed based on Weibull statistics. Further analysis of the mathematical structure of the model and experimental saccharification data showed the significance of the two parameters in this model. In particular, the λ value, defined the characteristic time, represents the overall performance of the saccharification system. This suggestion was further supported by statistical analysis of experimental saccharification data and analysis of the glucose production levels when λ and n values change. In conclusion, the constructed Weibull statistics-based model can accurately predict lignocellulose hydrolysis behavior and we can use the λ parameter to assess the overall performance of enzymatic lignocellulose degradation. Advantages and potential applications of the model and the λ value in saccharification performance assessment were discussed. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Using built environment characteristics to predict walking for exercise

PubMed Central

Lovasi, Gina S; Moudon, Anne V; Pearson, Amber L; Hurvitz, Philip M; Larson, Eric B; Siscovick, David S; Berke, Ethan M; Lumley, Thomas; Psaty, Bruce M

2008-01-01

Background Environments conducive to walking may help people avoid sedentary lifestyles and associated diseases. Recent studies developed walkability models combining several built environment characteristics to optimally predict walking. Developing and testing such models with the same data could lead to overestimating one's ability to predict walking in an independent sample of the population. More accurate estimates of model fit can be obtained by splitting a single study population into training and validation sets (holdout approach) or through developing and evaluating models in different populations. We used these two approaches to test whether built environment characteristics near the home predict walking for exercise. Study participants lived in western Washington State and were adult members of a health maintenance organization. The physical activity data used in this study were collected by telephone interview and were selected for their relevance to cardiovascular disease. In order to limit confounding by prior health conditions, the sample was restricted to participants in good self-reported health and without a documented history of cardiovascular disease. Results For 1,608 participants meeting the inclusion criteria, the mean age was 64 years, 90 percent were white, 37 percent had a college degree, and 62 percent of participants reported that they walked for exercise. Single built environment characteristics, such as residential density or connectivity, did not significantly predict walking for exercise. Regression models using multiple built environment characteristics to predict walking were not successful at predicting walking for exercise in an independent population sample. In the validation set, none of the logistic models had a C-statistic confidence interval excluding the null value of 0.5, and none of the linear models explained more than one percent of the variance in time spent walking for exercise. We did not detect significant differences in walking for exercise among census areas or postal codes, which were used as proxies for neighborhoods. Conclusion None of the built environment characteristics significantly predicted walking for exercise, nor did combinations of these characteristics predict walking for exercise when tested using a holdout approach. These results reflect a lack of neighborhood-level variation in walking for exercise for the population studied. PMID:18312660

A new concept in seismic landslide hazard analysis for practical application

NASA Astrophysics Data System (ADS)

Lee, Chyi-Tyi

2017-04-01

A seismic landslide hazard model could be constructed using deterministic approach (Jibson et al., 2000) or statistical approach (Lee, 2014). Both approaches got landslide spatial probability under a certain return-period earthquake. In the statistical approach, our recent study found that there are common patterns among different landslide susceptibility models of the same region. The common susceptibility could reflect relative stability of slopes at a region; higher susceptibility indicates lower stability. Using the common susceptibility together with an earthquake event landslide inventory and a map of topographically corrected Arias intensity, we can build the relationship among probability of failure, Arias intensity and the susceptibility. This relationship can immediately be used to construct a seismic landslide hazard map for the region that the empirical relationship built. If the common susceptibility model is further normalized and the empirical relationship built with normalized susceptibility, then the empirical relationship may be practically applied to different region with similar tectonic environments and climate conditions. This could be feasible, when a region has no existing earthquake-induce landslide data to train the susceptibility model and to build the relationship. It is worth mentioning that a rain-induced landslide susceptibility model has common pattern similar to earthquake-induced landslide susceptibility in the same region, and is usable to build the relationship with an earthquake event landslide inventory and a map of Arias intensity. These will be introduced with examples in the meeting.

How to Use Value-Added Analysis to Improve Student Learning: A Field Guide for School and District Leaders

ERIC Educational Resources Information Center

Kennedy, Kate; Peters, Mary; Thomas, Mike

2012-01-01

Value-added analysis is the most robust, statistically significant method available for helping educators quantify student progress over time. This powerful tool also reveals tangible strategies for improving instruction. Built around the work of Battelle for Kids, this book provides a field-tested continuous improvement model for using…

Spatiotemporal Built-up Land Density Mapping Using Various Spectral Indices in Landsat-7 ETM+ and Landsat-8 OLI/TIRS (Case Study: Surakarta City)

NASA Astrophysics Data System (ADS)

Risky, Yanuar S.; Aulia, Yogi H.; Widayani, Prima

2017-12-01

Spectral indices variations support for rapid and accurate extracting information such as built-up density. However, the exact determination of spectral waves for built-up density extraction is lacking. This study explains and compares the capabilities of 5 variations of spectral indices in spatiotemporal built-up density mapping using Landsat-7 ETM+ and Landsat-8 OLI/TIRS in Surakarta City on 2002 and 2015. The spectral indices variations used are 3 mid-infrared (MIR) based indices such as the Normalized Difference Built-up Index (NDBI), Urban Index (UI) and Built-up and 2 visible based indices such as VrNIR-BI (visible red) and VgNIR-BI (visible green). Linear regression statistics between ground value samples from Google Earth image in 2002 and 2015 and spectral indices for determining built-up land density. Ground value used amounted to 27 samples for model and 7 samples for accuracy test. The classification of built-up density mapping is divided into 9 classes: unclassified, 0-12.5%, 12.5-25%, 25-37.5%, 37.5-50%, 50-62.5%, 62.5-75%, 75-87.5% and 87.5-100 %. Accuracy of built-up land density mapping in 2002 and 2015 using VrNIR-BI (81,823% and 73.235%), VgNIR-BI (78.934% and 69.028%), NDBI (34.870% and 74.365%), UI (43.273% and 64.398%) and Built-up (59.755% and 72.664%). Based all spectral indices, Surakarta City on 2000-2015 has increased of built-up land density. VgNIR-BI has better capabilities for built-up land density mapping on Landsat-7 ETM + and Landsat-8 OLI/TIRS.

Parental perceived built environment measures and active play in Washington DC metropolitan children.

PubMed

Roberts, Jennifer D; Knight, Brandon; Ray, Rashawn; Saelens, Brian E

2016-06-01

Previous research identified associations between perceived built environment and adult physical activity; however, fewer studies have explored associations in children. The Built Environment and Active Play (BEAP) Study examined relationships between children's active play and parental perceptions of home neighborhood built environments within the Washington, DC metropolitan area (DMV). With this cross-sectional study, a questionnaire was administered in 2014 to parents of children (7-12 years old) residing in the DMV. Data were collected on children's active play, home built environment parental perceptions, and demographics. Active play response data were dichotomized by whether the child did or did not meet the 60-min/day Physical Activity Guidelines for Americans (PAGAs) recommendation. Perceived home neighborhood built environment data were also dichotomized. Chi-square tests determined differences in parental perceived built environment measures between active and non-active child groups. Logistic regression assessed the association of parental perceived built environment variables with active play while adjusting for demographic variables. The BEAP Study population (n = 144) included a uniquely diverse population of children with 23.7% African Americans and 10.4% Asian Americans. A statistically significant greater proportion of active children's parents agreed with the importance of neighborhood esthetics, active play areas, walkability and safety as compared to the parents of non-active children. Fully adjusted logistic regression models demonstrated that some parental perceived built environment measures (e.g. access to play equipment) were predictors of their children meeting the 60-min/day PAGA recommendation. Our findings support the important role of home neighborhood built environment perceptions on childhood active play.

Liver segmentation from CT images using a sparse priori statistical shape model (SP-SSM).

PubMed

Wang, Xuehu; Zheng, Yongchang; Gan, Lan; Wang, Xuan; Sang, Xinting; Kong, Xiangfeng; Zhao, Jie

2017-01-01

This study proposes a new liver segmentation method based on a sparse a priori statistical shape model (SP-SSM). First, mark points are selected in the liver a priori model and the original image. Then, the a priori shape and its mark points are used to obtain a dictionary for the liver boundary information. Second, the sparse coefficient is calculated based on the correspondence between mark points in the original image and those in the a priori model, and then the sparse statistical model is established by combining the sparse coefficients and the dictionary. Finally, the intensity energy and boundary energy models are built based on the intensity information and the specific boundary information of the original image. Then, the sparse matching constraint model is established based on the sparse coding theory. These models jointly drive the iterative deformation of the sparse statistical model to approximate and accurately extract the liver boundaries. This method can solve the problems of deformation model initialization and a priori method accuracy using the sparse dictionary. The SP-SSM can achieve a mean overlap error of 4.8% and a mean volume difference of 1.8%, whereas the average symmetric surface distance and the root mean square symmetric surface distance can reach 0.8 mm and 1.4 mm, respectively.

Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

NASA Astrophysics Data System (ADS)

Minh, Vu Trieu; Katushin, Dmitri; Antonov, Maksim; Veinthal, Renno

2017-03-01

This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM) based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock brittleness index (BI), the distance between planes of weakness (DPW), and the alpha angle (Alpha) between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP). Four (4) statistical regression models (two linear and two nonlinear) are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2) of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.

A Comparison between the WATCH Flare Data Statistical Properties and Predictions of the Statistical Flare Model

NASA Astrophysics Data System (ADS)

Crosby, N.; Georgoulis, M.; Vilmer, N.

1999-10-01

Solar burst observations in the deka-keV energy range originating from the WATCH experiment aboard the GRANAT spacecraft were used to perform frequency distributions built on measured X-ray flare parameters (Crosby et al., 1998). The results of the study show that: 1- the overall distribution functions are robust power laws extending over a number of decades. The typical parameters of events (total counts, peak count rates, duration) are all correlated to each other. 2- the overall distribution functions are the convolution of significantly different distribution functions built on parts of the whole data set filtered by the event duration. These "partial" frequency distributions are still power law distributions over several decades, with a slope systematically decreasing with increasing duration. 3- No correlation is found between the elapsed time interval between successive bursts arising from the same active region and the peak intensity of the flare. In this paper, we attempt a tentative comparison between the statistical properties of the self-organized critical (SOC) cellular automaton statistical flare models (see e.g. Lu and Hamilton (1991), Georgoulis and Vlahos (1996, 1998)) and the respective properties of the WATCH flare data. Despite the inherent weaknesses of the SOC models to simulate a number of physical processes in the active region, it is found that most of the observed statistical properties can be reproduced using the SOC models, including the various frequency distributions and scatter plots. We finally conclude that, even if SOC models must be refined to improve the physical links to MHD approaches, they nevertheless represent a good approach to describe the properties of rapid energy dissipation and magnetic field annihilation in complex and magnetized plasmas. Crosby N., Vilmer N., Lund N. and Sunyaev R., A&A; 334; 299-313; 1998 Crosby N., Lund N., Vilmer N. and Sunyaev R.; A&A Supplement Series; 130, 233, 1998 Georgoulis M. and Vlahos L., 1996, Astrophy. J. Letters, 469, L135 Georgoulis M. and Vlahos L., 1998, in preparation Lu E.T. and Hamilton R.J., 1991, Astroph. J., 380, L89

A new multiple regression model to identify multi-family houses with a high prevalence of sick building symptoms "SBS", within the healthy sustainable house study in Stockholm (3H).

PubMed

Engvall, Karin; Hult, M; Corner, R; Lampa, E; Norbäck, D; Emenius, G

2010-01-01

The aim was to develop a new model to identify residential buildings with higher frequencies of "SBS" than expected, "risk buildings". In 2005, 481 multi-family buildings with 10,506 dwellings in Stockholm were studied by a new stratified random sampling. A standardised self-administered questionnaire was used to assess "SBS", atopy and personal factors. The response rate was 73%. Statistical analysis was performed by multiple logistic regressions. Dwellers owning their building reported less "SBS" than those renting. There was a strong relationship between socio-economic factors and ownership. The regression model, ended up with high explanatory values for age, gender, atopy and ownership. Applying our model, 9% of all residential buildings in Stockholm were classified as "risk buildings" with the highest proportion in houses built 1961-1975 (26%) and lowest in houses built 1985-1990 (4%). To identify "risk buildings", it is necessary to adjust for ownership and population characteristics.

Prediction of barrier island restoration response and its interactions with the natural environment

NASA Astrophysics Data System (ADS)

Plant, N. G.; Stockdon, H. F.; Flocks, J.; Sallenger, A. H.; Long, J. W.; Cormier, J. M.; Guy, K.; Thompson, D. M.

2012-12-01

A 2-meter high sand berm was constructed along Chandeleur Island, Louisiana, in an attempt to provide protection against the Deepwater Horizon oil spill. Berm construction started in June 2010 and ended in April 2011. Variations in both island morphology and construction of the 15-km long berm resulted in the development of four different morphologies: a berm built on a submerged island platform to the north of the existing island, a berm built seaward of the existing island, a berm built along the island shoreline, and portions of the island where no berm was constructed. These different morphologies provide a natural laboratory for testing the understanding of berm and barrier island response to storms. In particular, the ability to predict berm evolution using statistical modeling of the interactions between the island, berm, and oceanographic processes was tested. This particular test was part of a broader USGS research effort to understand processes that bridge the gap between short-term storm response and longer-term geologic and climate interactions that shape barrier-island systems. Berm construction and subsequent berm and island evolution were monitored using satellite and aerial remote sensing and topographic and bathymetric surveys. To date, significant berm evolution occurred in both the north (including terminal erosion, overwash, and a large breach), center (overwash and numerous breaches), and south (overwash). The response of the central portion of the berm to winter and tropical storms was significant such that none of the residual berm remained within its construction footprint. The evolution of the central portion of the berm was well predicted using a statistical modeling approach that used predicted and modeled wave conditions to identify the likelihood of overwash events. Comparison of different modeled evolution scenarios to the one that was observed showed that berm response was sensitive to the frequency and severity of winter and tropical storms. These findings demonstrate an observation and modeling approach that can be applied to understanding and managing other natural and restored barrier islands.

Current Risk Adjustment and Comorbidity Index Underperformance in Predicting Post-Acute Utilization and Hospital Readmissions After Joint Replacements: Implications for Comprehensive Care for Joint Replacement Model.

PubMed

Kumar, Amit; Karmarkar, Amol; Downer, Brian; Vashist, Amit; Adhikari, Deepak; Al Snih, Soham; Ottenbacher, Kenneth

2017-11-01

To compare the performances of 3 comorbidity indices, the Charlson Comorbidity Index, the Elixhauser Comorbidity Index, and the Centers for Medicare & Medicaid Services (CMS) risk adjustment model, Hierarchical Condition Category (HCC), in predicting post-acute discharge settings and hospital readmission for patients after joint replacement. A retrospective study of Medicare beneficiaries with total knee replacement (TKR) or total hip replacement (THR) discharged from hospitals in 2009-2011 (n = 607,349) was performed. Study outcomes were post-acute discharge setting and unplanned 30-, 60-, and 90-day hospital readmissions. Logistic regression models were built to compare the performance of the 3 comorbidity indices using C statistics. The base model included patient demographics and hospital use. Subsequent models included 1 of the 3 comorbidity indices. Additional multivariable logistic regression models were built to identify individual comorbid conditions associated with high risk of hospital readmissions. The 30-, 60-, and 90-day unplanned hospital readmission rates were 5.3%, 7.2%, and 8.5%, respectively. Patients were most frequently discharged to home health (46.3%), followed by skilled nursing facility (40.9%) and inpatient rehabilitation facility (12.7%). The C statistics for the base model in predicting post-acute discharge setting and 30-, 60-, and 90-day readmission in TKR and THR were between 0.63 and 0.67. Adding the Charlson Comorbidity Index, the Elixhauser Comorbidity Index, or HCC increased the C statistic minimally from the base model for predicting both discharge settings and hospital readmission. The health conditions most frequently associated with hospital readmission were diabetes mellitus, pulmonary disease, arrhythmias, and heart disease. The comorbidity indices and CMS-HCC demonstrated weak discriminatory ability to predict post-acute discharge settings and hospital readmission following joint replacement. © 2017, American College of Rheumatology.

Statistical learning theory for high dimensional prediction: Application to criterion-keyed scale development.

PubMed

Chapman, Benjamin P; Weiss, Alexander; Duberstein, Paul R

2016-12-01

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in "big data" problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how 3 common SLT algorithms-supervised principal components, regularization, and boosting-can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach-or perhaps because of them-SLT methods may hold value as a statistically rigorous approach to exploratory regression. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

The consentaneous model of the financial markets exhibiting spurious nature of long-range memory

NASA Astrophysics Data System (ADS)

Gontis, V.; Kononovicius, A.

2018-09-01

It is widely accepted that there is strong persistence in the volatility of financial time series. The origin of the observed persistence, or long-range memory, is still an open problem as the observed phenomenon could be a spurious effect. Earlier we have proposed the consentaneous model of the financial markets based on the non-linear stochastic differential equations. The consentaneous model successfully reproduces empirical probability and power spectral densities of volatility. This approach is qualitatively different from models built using fractional Brownian motion. In this contribution we investigate burst and inter-burst duration statistics of volatility in the financial markets employing the consentaneous model. Our analysis provides an evidence that empirical statistical properties of burst and inter-burst duration can be explained by non-linear stochastic differential equations driving the volatility in the financial markets. This serves as an strong argument that long-range memory in finance can have spurious nature.

Modeled Neutron Induced Nuclear Reaction Cross Sections for Radiochemsitry in the region of Thulium, Lutetium, and Tantalum I. Results of Built in Spherical Symmetry in a Deformed Region

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hoffman, R. D.

2013-09-06

We have developed a set of modeled nuclear reaction cross sections for use in radiochemical diagnostics. Systematics for the input parameters required by the Hauser-Feshbach statistical model were developed and used to calculate neutron induced nuclear reaction cross sections for targets ranging from Terbium (Z = 65) to Rhenium (Z = 75). Of particular interest are the cross sections on Tm, Lu, and Ta including reactions on isomeric targets.

Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity.

PubMed

Webb, Samuel J; Hanser, Thierry; Howlin, Brendan; Krause, Paul; Vessey, Jonathan D

2014-03-25

A new algorithm has been developed to enable the interpretation of black box models. The developed algorithm is agnostic to learning algorithm and open to all structural based descriptors such as fragments, keys and hashed fingerprints. The algorithm has provided meaningful interpretation of Ames mutagenicity predictions from both random forest and support vector machine models built on a variety of structural fingerprints.A fragmentation algorithm is utilised to investigate the model's behaviour on specific substructures present in the query. An output is formulated summarising causes of activation and deactivation. The algorithm is able to identify multiple causes of activation or deactivation in addition to identifying localised deactivations where the prediction for the query is active overall. No loss in performance is seen as there is no change in the prediction; the interpretation is produced directly on the model's behaviour for the specific query. Models have been built using multiple learning algorithms including support vector machine and random forest. The models were built on public Ames mutagenicity data and a variety of fingerprint descriptors were used. These models produced a good performance in both internal and external validation with accuracies around 82%. The models were used to evaluate the interpretation algorithm. Interpretation was revealed that links closely with understood mechanisms for Ames mutagenicity. This methodology allows for a greater utilisation of the predictions made by black box models and can expedite further study based on the output for a (quantitative) structure activity model. Additionally the algorithm could be utilised for chemical dataset investigation and knowledge extraction/human SAR development.

Automated Reporting of DXA Studies Using a Custom-Built Computer Program.

PubMed

England, Joseph R; Colletti, Patrick M

2018-06-01

Dual-energy x-ray absorptiometry (DXA) scans are a critical population health tool and relatively simple to interpret but can be time consuming to report, often requiring manual transfer of bone mineral density and associated statistics into commercially available dictation systems. We describe here a custom-built computer program for automated reporting of DXA scans using Pydicom, an open-source package built in the Python computer language, and regular expressions to mine DICOM tags for patient information and bone mineral density statistics. This program, easy to emulate by any novice computer programmer, has doubled our efficiency at reporting DXA scans and has eliminated dictation errors.

Anyonic braiding in optical lattices

PubMed Central

Zhang, Chuanwei; Scarola, V. W.; Tewari, Sumanta; Das Sarma, S.

2007-01-01

Topological quantum states of matter, both Abelian and non-Abelian, are characterized by excitations whose wavefunctions undergo nontrivial statistical transformations as one excitation is moved (braided) around another. Topological quantum computation proposes to use the topological protection and the braiding statistics of a non-Abelian topological state to perform quantum computation. The enormous technological prospect of topological quantum computation provides new motivation for experimentally observing a topological state. Here, we explicitly work out a realistic experimental scheme to create and braid the Abelian topological excitations in the Kitaev model built on a tunable robust system, a cold atom optical lattice. We also demonstrate how to detect the key feature of these excitations: their braiding statistics. Observation of this statistics would directly establish the existence of anyons, quantum particles that are neither fermions nor bosons. In addition to establishing topological matter, the experimental scheme we develop here can also be adapted to a non-Abelian topological state, supported by the same Kitaev model but in a different parameter regime, to eventually build topologically protected quantum gates. PMID:18000038

Statistical text classifier to detect specific type of medical incidents.

PubMed

Wong, Zoie Shui-Yee; Akiyama, Masanori

2013-01-01

WHO Patient Safety has put focus to increase the coherence and expressiveness of patient safety classification with the foundation of International Classification for Patient Safety (ICPS). Text classification and statistical approaches has showed to be successful to identifysafety problems in the Aviation industryusing incident text information. It has been challenging to comprehend the taxonomy of medical incidents in a structured manner. Independent reporting mechanisms for patient safety incidents have been established in the UK, Canada, Australia, Japan, Hong Kong etc. This research demonstrates the potential to construct statistical text classifiers to detect specific type of medical incidents using incident text data. An illustrative example for classifying look-alike sound-alike (LASA) medication incidents using structured text from 227 advisories related to medication errors from Global Patient Safety Alerts (GPSA) is shown in this poster presentation. The classifier was built using logistic regression model. ROC curve and the AUC value indicated that this is a satisfactory good model.

A Backward-Lagrangian-Stochastic Footprint Model for the Urban Environment

NASA Astrophysics Data System (ADS)

Wang, Chenghao; Wang, Zhi-Hua; Yang, Jiachuan; Li, Qi

2018-02-01

Built terrains, with their complexity in morphology, high heterogeneity, and anthropogenic impact, impose substantial challenges in Earth-system modelling. In particular, estimation of the source areas and footprints of atmospheric measurements in cities requires realistic representation of the landscape characteristics and flow physics in urban areas, but has hitherto been heavily reliant on large-eddy simulations. In this study, we developed physical parametrization schemes for estimating urban footprints based on the backward-Lagrangian-stochastic algorithm, with the built environment represented by street canyons. The vertical profile of mean streamwise velocity is parametrized for the urban canopy and boundary layer. Flux footprints estimated by the proposed model show reasonable agreement with analytical predictions over flat surfaces without roughness elements, and with experimental observations over sparse plant canopies. Furthermore, comparisons of canyon flow and turbulence profiles and the subsequent footprints were made between the proposed model and large-eddy simulation data. The results suggest that the parametrized canyon wind and turbulence statistics, based on the simple similarity theory used, need to be further improved to yield more realistic urban footprint modelling.

Bayesian mixture modeling for blood sugar levels of diabetes mellitus patients (case study in RSUD Saiful Anwar Malang Indonesia)

NASA Astrophysics Data System (ADS)

Budi Astuti, Ani; Iriawan, Nur; Irhamah; Kuswanto, Heri; Sasiarini, Laksmi

2017-10-01

Bayesian statistics proposes an approach that is very flexible in the number of samples and distribution of data. Bayesian Mixture Model (BMM) is a Bayesian approach for multimodal models. Diabetes Mellitus (DM) is more commonly known in the Indonesian community as sweet pee. This disease is one type of chronic non-communicable diseases but it is very dangerous to humans because of the effects of other diseases complications caused. WHO reports in 2013 showed DM disease was ranked 6th in the world as the leading causes of human death. In Indonesia, DM disease continues to increase over time. These research would be studied patterns and would be built the BMM models of the DM data through simulation studies where the simulation data built on cases of blood sugar levels of DM patients in RSUD Saiful Anwar Malang. The results have been successfully demonstrated pattern of distribution of the DM data which has a normal mixture distribution. The BMM models have succeed to accommodate the real condition of the DM data based on the data driven concept.

Using regression equations built from summary data in the psychological assessment of the individual case: extension to multiple regression.

PubMed

Crawford, John R; Garthwaite, Paul H; Denham, Annie K; Chelune, Gordon J

2012-12-01

Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because (a) not all psychologists are aware that regression equations can be built not only from raw data but also using only basic summary data for a sample, and (b) the computations involved are tedious and prone to error. In an attempt to overcome these barriers, Crawford and Garthwaite (2007) provided methods to build and apply simple linear regression models using summary statistics as data. In the present study, we extend this work to set out the steps required to build multiple regression models from sample summary statistics and the further steps required to compute the associated statistics for drawing inferences concerning an individual case. We also develop, describe, and make available a computer program that implements these methods. Although there are caveats associated with the use of the methods, these need to be balanced against pragmatic considerations and against the alternative of either entirely ignoring a pertinent data set or using it informally to provide a clinical "guesstimate." Upgraded versions of earlier programs for regression in the single case are also provided; these add the point and interval estimates of effect size developed in the present article.

Mining Twitter Data to Improve Detection of Schizophrenia

PubMed Central

McManus, Kimberly; Mallory, Emily K.; Goldfeder, Rachel L.; Haynes, Winston A.; Tatum, Jonathan D.

2015-01-01

Individuals who suffer from schizophrenia comprise I percent of the United States population and are four times more likely to die of suicide than the general US population. Identification of at-risk individuals with schizophrenia is challenging when they do not seek treatment. Microblogging platforms allow users to share their thoughts and emotions with the world in short snippets of text. In this work, we leveraged the large corpus of Twitter posts and machine-learning methodologies to detect individuals with schizophrenia. Using features from tweets such as emoticon use, posting time of day, and dictionary terms, we trained, built, and validated several machine learning models. Our support vector machine model achieved the best performance with 92% precision and 71% recall on the held-out test set. Additionally, we built a web application that dynamically displays summary statistics between cohorts. This enables outreach to undiagnosed individuals, improved physician diagnoses, and destigmatization of schizophrenia. PMID:26306253

A New Structure-Activity Relationship (SAR) Model for Predicting Drug-Induced Liver Injury, Based on Statistical and Expert-Based Structural Alerts

PubMed Central

Pizzo, Fabiola; Lombardo, Anna; Manganaro, Alberto; Benfenati, Emilio

2016-01-01

The prompt identification of chemical molecules with potential effects on liver may help in drug discovery and in raising the levels of protection for human health. Besides in vitro approaches, computational methods in toxicology are drawing attention. We built a structure-activity relationship (SAR) model for evaluating hepatotoxicity. After compiling a data set of 950 compounds using data from the literature, we randomly split it into training (80%) and test sets (20%). We also compiled an external validation set (101 compounds) for evaluating the performance of the model. To extract structural alerts (SAs) related to hepatotoxicity and non-hepatotoxicity we used SARpy, a statistical application that automatically identifies and extracts chemical fragments related to a specific activity. We also applied the chemical grouping approach for manually identifying other SAs. We calculated accuracy, specificity, sensitivity and Matthews correlation coefficient (MCC) on the training, test and external validation sets. Considering the complexity of the endpoint, the model performed well. In the training, test and external validation sets the accuracy was respectively 81, 63, and 68%, specificity 89, 33, and 33%, sensitivity 93, 88, and 80% and MCC 0.63, 0.27, and 0.13. Since it is preferable to overestimate hepatotoxicity rather than not to recognize unsafe compounds, the model's architecture followed a conservative approach. As it was built using human data, it might be applied without any need for extrapolation from other species. This model will be freely available in the VEGA platform. PMID:27920722

Extraction of business relationships in supply networks using statistical learning theory.

PubMed

Zuo, Yi; Kajikawa, Yuya; Mori, Junichiro

2016-06-01

Supply chain management represents one of the most important scientific streams of operations research. The supply of energy, materials, products, and services involves millions of transactions conducted among national and local business enterprises. To deliver efficient and effective support for supply chain design and management, structural analyses and predictive models of customer-supplier relationships are expected to clarify current enterprise business conditions and to help enterprises identify innovative business partners for future success. This article presents the outcomes of a recent structural investigation concerning a supply network in the central area of Japan. We investigated the effectiveness of statistical learning theory to express the individual differences of a supply chain of enterprises within a certain business community using social network analysis. In the experiments, we employ support vector machine to train a customer-supplier relationship model on one of the main communities extracted from a supply network in the central area of Japan. The prediction results reveal an F-value of approximately 70% when the model is built by using network-based features, and an F-value of approximately 77% when the model is built by using attribute-based features. When we build the model based on both, F-values are improved to approximately 82%. The results of this research can help to dispel the implicit design space concerning customer-supplier relationships, which can be explored and refined from detailed topological information provided by network structures rather than from traditional and attribute-related enterprise profiles. We also investigate and discuss differences in the predictive accuracy of the model for different sizes of enterprises and types of business communities.

Geospatial analysis of spaceborne remote sensing data for assessing disaster impacts and modeling surface runoff in the built-environment

NASA Astrophysics Data System (ADS)

Wodajo, Bikila Teklu

Every year, coastal disasters such as hurricanes and floods claim hundreds of lives and severely damage homes, businesses, and lifeline infrastructure. This research was motivated by the 2005 Hurricane Katrina disaster, which devastated the Mississippi and Louisiana Gulf Coast. The primary objective was to develop a geospatial decision-support system for extracting built-up surfaces and estimating disaster impacts using spaceborne remote sensing satellite imagery. Pre-Katrina 1-m Ikonos imagery of a 5km x 10km area of Gulfport, Mississippi, was used as source data to develop the built-up area and natural surfaces or BANS classification methodology. Autocorrelation of 0.6 or higher values related to spectral reflectance values of groundtruth pixels were used to select spectral bands and establish the BANS decision criteria of unique ranges of reflectance values. Surface classification results using GeoMedia Pro geospatial analysis for Gulfport sample areas, based on BANS criteria and manually drawn polygons, were within +/-7% of the groundtruth. The difference between the BANS results and the groundtruth was statistically not significant. BANS is a significant improvement over other supervised classification methods, which showed only 50% correctly classified pixels. The storm debris and erosion estimation or SDE methodology was developed from analysis of pre- and post-Katrina surface classification results of Gulfport samples. The SDE severity level criteria considered hurricane and flood damages and vulnerability of inhabited built-environment. A linear regression model, with +0.93 Pearson R-value, was developed for predicting SDE as a function of pre-disaster percent built-up area. SDE predictions for Gulfport sample areas, used for validation, were within +/-4% of calculated values. The damage cost model considered maintenance, rehabilitation and reconstruction costs related to infrastructure damage and community impacts of Hurricane Katrina. The developed models were implemented for a study area along I-10 considering the predominantly flood-induced damages in New Orleans. The BANS methodology was calibrated for 0.6-m QuickBird2 multispectral imagery of Karachi Port area in Pakistan. The results were accurate within +/-6% of the groundtruth. Due to its computational simplicity, the unit hydrograph method is recommended for geospatial visualization of surface runoff in the built-environment using BANS surface classification maps and elevations data. Key words. geospatial analysis, satellite imagery, built-environment, hurricane, disaster impacts, runoff.

Strategies for Reduced-Order Models in Uncertainty Quantification of Complex Turbulent Dynamical Systems

NASA Astrophysics Data System (ADS)

Qi, Di

Turbulent dynamical systems are ubiquitous in science and engineering. Uncertainty quantification (UQ) in turbulent dynamical systems is a grand challenge where the goal is to obtain statistical estimates for key physical quantities. In the development of a proper UQ scheme for systems characterized by both a high-dimensional phase space and a large number of instabilities, significant model errors compared with the true natural signal are always unavoidable due to both the imperfect understanding of the underlying physical processes and the limited computational resources available. One central issue in contemporary research is the development of a systematic methodology for reduced order models that can recover the crucial features both with model fidelity in statistical equilibrium and with model sensitivity in response to perturbations. In the first part, we discuss a general mathematical framework to construct statistically accurate reduced-order models that have skill in capturing the statistical variability in the principal directions of a general class of complex systems with quadratic nonlinearity. A systematic hierarchy of simple statistical closure schemes, which are built through new global statistical energy conservation principles combined with statistical equilibrium fidelity, are designed and tested for UQ of these problems. Second, the capacity of imperfect low-order stochastic approximations to model extreme events in a passive scalar field advected by turbulent flows is investigated. The effects in complicated flow systems are considered including strong nonlinear and non-Gaussian interactions, and much simpler and cheaper imperfect models with model error are constructed to capture the crucial statistical features in the stationary tracer field. Several mathematical ideas are introduced to improve the prediction skill of the imperfect reduced-order models. Most importantly, empirical information theory and statistical linear response theory are applied in the training phase for calibrating model errors to achieve optimal imperfect model parameters; and total statistical energy dynamics are introduced to improve the model sensitivity in the prediction phase especially when strong external perturbations are exerted. The validity of reduced-order models for predicting statistical responses and intermittency is demonstrated on a series of instructive models with increasing complexity, including the stochastic triad model, the Lorenz '96 model, and models for barotropic and baroclinic turbulence. The skillful low-order modeling methods developed here should also be useful for other applications such as efficient algorithms for data assimilation.

Statistical modelling of Fat, Oil and Grease (FOG) deposits in wastewater pump sumps.

PubMed

Nieuwenhuis, Eva; Post, Johan; Duinmeijer, Alex; Langeveld, Jeroen; Clemens, François

2018-05-15

The accumulation of FOG (Fat, Oil and Grease) deposits in sewer pumping stations results in an increase in maintenance costs, malfunctioning of pumps and, a potential increase of wastewater spills in receiving open water bodies. It is thought that a variety of parameters (e.g. geometry of the pump sump, pump operation, socioeconomic parameters of the catchment) influences the built-up of FOG. Based on a database containing data of 126 pumping stations located in five Dutch municipalities a statistical model was built. It is shown that 3 parameters are most significant in explaining the occurrence of FOG deposits: mean income of the population in a catchment, the amount of energy (kinetic and potential) per m 3 per day and the density of restaurants, bars and hotels in a catchment. Further it is shown that there are significant differences between municipalities that can be traced back to the local 'design paradigm'. For example, in Amsterdam, the design philosophy of discharging in the pump sump under the water surface (and hence maintaining a low level of turbulence in the pump sump) results in an increase of the probability of the formation of FOG. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Characterization and spatial modeling of urban sprawl in the Wuhan Metropolitan Area, China

NASA Astrophysics Data System (ADS)

Zeng, Chen; Liu, Yaolin; Stein, Alfred; Jiao, Limin

2015-02-01

Urban sprawl has led to environmental problems and large losses of arable land in China. In this study, we monitor and model urban sprawl by means of a combination of remote sensing, geographical information system and spatial statistics. We use time-series data to explore the potential socio-economic driving forces behind urban sprawl, and spatial models in different scenarios to explore the spatio-temporal interactions. The methodology is applied to the city of Wuhan, China, for the period from 1990 to 2013. The results reveal that the built-up land has expanded and has dispersed in urban clusters. Population growth, and economic and transportation development are still the main causes of urban sprawl; however, when they have developed to certain levels, the area affected by construction in urban areas (Jian Cheng Qu (JCQ)) and the area of cultivated land (ACL) tend to be stable. Spatial regression models are shown to be superior to the traditional models. The interaction among districts with the same administrative status is stronger than if one of those neighbors is in the city center and the other in the suburban area. The expansion of urban built-up land is driven by the socio-economic development at the same period, and greatly influenced by its spatio-temporal neighbors. We conclude that the integration of remote sensing, a geographical information system, and spatial statistics offers an excellent opportunity to explore the spatio-temporal variation and interactions among the districts in the sprawling metropolitan areas. Relevant regulations to control the urban sprawl process are suggested accordingly.

Statistical model for speckle pattern optimization.

PubMed

Su, Yong; Zhang, Qingchuan; Gao, Zeren

2017-11-27

Image registration is the key technique of optical metrologies such as digital image correlation (DIC), particle image velocimetry (PIV), and speckle metrology. Its performance depends critically on the quality of image pattern, and thus pattern optimization attracts extensive attention. In this article, a statistical model is built to optimize speckle patterns that are composed of randomly positioned speckles. It is found that the process of speckle pattern generation is essentially a filtered Poisson process. The dependence of measurement errors (including systematic errors, random errors, and overall errors) upon speckle pattern generation parameters is characterized analytically. By minimizing the errors, formulas of the optimal speckle radius are presented. Although the primary motivation is from the field of DIC, we believed that scholars in other optical measurement communities, such as PIV and speckle metrology, will benefit from these discussions.

Time series evaluation of landscape dynamics using annual Landsat imagery and spatial statistical modeling: Evidence from the Phoenix metropolitan region

NASA Astrophysics Data System (ADS)

Fan, Chao; Myint, Soe W.; Rey, Sergio J.; Li, Wenwen

2017-06-01

Urbanization is a natural and social process involving simultaneous changes to the Earth's land systems, energy flow, demographics, and the economy. Understanding the spatiotemporal pattern of urbanization is increasingly important for policy formulation, decision making, and natural resource management. A combination of satellite remote sensing and patch-based models has been widely adopted to characterize landscape changes at various spatial and temporal scales. Nevertheless, the validity of this type of framework in identifying long-term changes, especially subtle or gradual land modifications is seriously challenged. In this paper, we integrate annual image time series, continuous spatial indices, and non-parametric trend analysis into a spatiotemporal study of landscape dynamics over the Phoenix metropolitan area from 1991 to 2010. We harness local indicators of spatial dependence and modified Mann-Kendall test to describe the monotonic trends in the quantity and spatial arrangement of two important land use land cover types: vegetation and built-up areas. Results suggest that declines in vegetation and increases in built-up areas are the two prevalent types of changes across the region. Vegetation increases mostly occur at the outskirts where new residential areas are developed from natural desert. A sizable proportion of vegetation declines and built-up increases are seen in the central and southeast part. Extensive land conversion from agricultural fields into urban land use is one important driver of vegetation declines. The xeriscaping practice also contributes to part of vegetation loss and an increasingly heterogeneous landscape. The quantitative framework proposed in this study provides a pathway to effective landscape mapping and change monitoring from a spatial statistical perspective.

Reverse engineering systems models of regulation: discovery, prediction and mechanisms.

PubMed

Ashworth, Justin; Wurtmann, Elisabeth J; Baliga, Nitin S

2012-08-01

Biological systems can now be understood in comprehensive and quantitative detail using systems biology approaches. Putative genome-scale models can be built rapidly based upon biological inventories and strategic system-wide molecular measurements. Current models combine statistical associations, causative abstractions, and known molecular mechanisms to explain and predict quantitative and complex phenotypes. This top-down 'reverse engineering' approach generates useful organism-scale models despite noise and incompleteness in data and knowledge. Here we review and discuss the reverse engineering of biological systems using top-down data-driven approaches, in order to improve discovery, hypothesis generation, and the inference of biological properties. Copyright © 2011 Elsevier Ltd. All rights reserved.

Effects of Model Formulation on Estimates of Health in Individual Right Whales (Eubalaena glacialis).

PubMed

Schick, Robert S; Kraus, Scott D; Rolland, Rosalind M; Knowlton, Amy R; Hamilton, Philip K; Pettis, Heather M; Thomas, Len; Harwood, John; Clark, James S

2016-01-01

Right whales are vulnerable to many sources of anthropogenic disturbance including ship strikes, entanglement with fishing gear, and anthropogenic noise. The effect of these factors on individual health is unclear. A statistical model using photographic evidence of health was recently built to infer the true or hidden health of individual right whales. However, two important prior assumptions about the role of missing data and unexplained variance on the estimates were not previously assessed. Here we tested these factors by varying prior assumptions and model formulation. We found sensitivity to each assumption and used the output to make guidelines on future model formulation.

On the Relationship between Molecular Hit Rates in High-Throughput Screening and Molecular Descriptors.

PubMed

Hansson, Mari; Pemberton, John; Engkvist, Ola; Feierberg, Isabella; Brive, Lars; Jarvis, Philip; Zander-Balderud, Linda; Chen, Hongming

2014-06-01

High-throughput screening (HTS) is widely used in the pharmaceutical industry to identify novel chemical starting points for drug discovery projects. The current study focuses on the relationship between molecular hit rate in recent in-house HTS and four common molecular descriptors: lipophilicity (ClogP), size (heavy atom count, HEV), fraction of sp(3)-hybridized carbons (Fsp3), and fraction of molecular framework (f(MF)). The molecular hit rate is defined as the fraction of times the molecule has been assigned as active in the HTS campaigns where it has been screened. Beta-binomial statistical models were built to model the molecular hit rate as a function of these descriptors. The advantage of the beta-binomial statistical models is that the correlation between the descriptors is taken into account. Higher degree polynomial terms of the descriptors were also added into the beta-binomial statistic model to improve the model quality. The relative influence of different molecular descriptors on molecular hit rate has been estimated, taking into account that the descriptors are correlated to each other through applying beta-binomial statistical modeling. The results show that ClogP has the largest influence on the molecular hit rate, followed by Fsp3 and HEV. f(MF) has only a minor influence besides its correlation with the other molecular descriptors. © 2013 Society for Laboratory Automation and Screening.

A method for the inclusion of physical activity-related health benefits in cost-benefit analysis of built environment initiatives.

PubMed

Zapata-Diomedi, Belen; Gunn, Lucy; Giles-Corti, Billie; Shiell, Alan; Lennert Veerman, J

2018-01-01

The built environment has a significant influence on population levels of physical activity (PA) and therefore health. However, PA-related health benefits are seldom considered in transport and urban planning (i.e. built environment interventions) cost-benefit analysis. Cost-benefit analysis implies that the benefits of any initiative are valued in monetary terms to make them commensurable with costs. This leads to the need for monetised values of the health benefits of PA. The aim of this study was to explore a method for the incorporation of monetised PA-related health benefits in cost-benefit analysis of built environment interventions. Firstly, we estimated the change in population level of PA attributable to a change in the built environment due to the intervention. Then, changes in population levels of PA were translated into monetary values. For the first step we used estimates from the literature for the association of built environment features with physical activity outcomes. For the second step we used the multi-cohort proportional multi-state life table model to predict changes in health-adjusted life years and health care costs as a function of changes in PA. Finally, we monetised health-adjusted life years using the value of a statistical life year. Future research could adapt these methods to assess the health and economic impacts of specific urban development scenarios by working in collaboration with urban planners. Copyright © 2017 Elsevier Inc. All rights reserved.

Using Patient Demographics and Statistical Modeling to Predict Knee Tibia Component Sizing in Total Knee Arthroplasty.

PubMed

Ren, Anna N; Neher, Robert E; Bell, Tyler; Grimm, James

2018-06-01

Preoperative planning is important to achieve successful implantation in primary total knee arthroplasty (TKA). However, traditional TKA templating techniques are not accurate enough to predict the component size to a very close range. With the goal of developing a general predictive statistical model using patient demographic information, ordinal logistic regression was applied to build a proportional odds model to predict the tibia component size. The study retrospectively collected the data of 1992 primary Persona Knee System TKA procedures. Of them, 199 procedures were randomly selected as testing data and the rest of the data were randomly partitioned between model training data and model evaluation data with a ratio of 7:3. Different models were trained and evaluated on the training and validation data sets after data exploration. The final model had patient gender, age, weight, and height as independent variables and predicted the tibia size within 1 size difference 96% of the time on the validation data, 94% of the time on the testing data, and 92% on a prospective cadaver data set. The study results indicated the statistical model built by ordinal logistic regression can increase the accuracy of tibia sizing information for Persona Knee preoperative templating. This research shows statistical modeling may be used with radiographs to dramatically enhance the templating accuracy, efficiency, and quality. In general, this methodology can be applied to other TKA products when the data are applicable. Copyright © 2018 Elsevier Inc. All rights reserved.

Examination of a muscular activity estimation model using a Bayesian network for the influence of an ankle foot orthosis.

PubMed

Inoue, Jun; Kawamura, Kazuya; Fujie, Masakatsu G

2012-01-01

In the present paper, we examine the appropriateness of a new model to examine the activity of the foot in gait. We developed an estimation model for foot-ankle muscular activity in the design of an ankle-foot orthosis by means of a statistical method. We chose three muscles for measuring muscular activity and built a Bayesian network model to confirm the appropriateness of the estimation model. We experimentally examined the normal gait of a non-disabled subject. We measured the muscular activity of the lower foot muscles using electromyography, the joint angles, and the pressure on each part of the sole. From these data, we obtained the causal relationship at every 10% level for these factors and built models for the stance phase, control term, and propulsive term. Our model has three advantages. First, it can express the influences that change during gait because we use 10% level nodes for each factor. Second, it can express the influences of factors that differ for low and high muscular-activity levels. Third, we created divided models that are able to reflect the actual features of gait. In evaluating the new model, we confirmed it is able to estimate all muscular activity level with an accuracy of over 90%.

Hierarchical Distributed-Lag Models: Exploring Varying Geographic Scale and Magnitude in Associations Between the Built Environment and Health

PubMed Central

Baek, Jonggyu; Sanchez-Vaznaugh, Emma V.; Sánchez, Brisa N.

2016-01-01

It is well known that associations between features of the built environment and health depend on the geographic scale used to construct environmental attributes. In the built environment literature, it has long been argued that geographic scales may vary across study locations. However, this hypothesized variation has not been systematically examined due to a lack of available statistical methods. We propose a hierarchical distributed-lag model (HDLM) for estimating the underlying overall shape of food environment–health associations as a function of distance from locations of interest. This method enables indirect assessment of relevant geographic scales and captures area-level heterogeneity in the magnitudes of associations, along with relevant distances within areas. The proposed model was used to systematically examine area-level variation in the association between availability of convenience stores around schools and children's weights. For this case study, body mass index (weight kg)/height (m)2) z scores (BMIz) for 7th grade children collected via California's 2001–2009 FitnessGram testing program were linked to a commercial database that contained locations of food outlets statewide. Findings suggested that convenience store availability may influence BMIz only in some places and at varying distances from schools. Future research should examine localized environmental or policy differences that may explain the heterogeneity in convenience store–BMIz associations. PMID:26888753

Built-up Areas Extraction in High Resolution SAR Imagery based on the method of Multiple Feature Weighted Fusion

NASA Astrophysics Data System (ADS)

Liu, X.; Zhang, J. X.; Zhao, Z.; Ma, A. D.

2015-06-01

Synthetic aperture radar in the application of remote sensing technology is becoming more and more widely because of its all-time and all-weather operation, feature extraction research in high resolution SAR image has become a hot topic of concern. In particular, with the continuous improvement of airborne SAR image resolution, image texture information become more abundant. It's of great significance to classification and extraction. In this paper, a novel method for built-up areas extraction using both statistical and structural features is proposed according to the built-up texture features. First of all, statistical texture features and structural features are respectively extracted by classical method of gray level co-occurrence matrix and method of variogram function, and the direction information is considered in this process. Next, feature weights are calculated innovatively according to the Bhattacharyya distance. Then, all features are weighted fusion. At last, the fused image is classified with K-means classification method and the built-up areas are extracted after post classification process. The proposed method has been tested by domestic airborne P band polarization SAR images, at the same time, two groups of experiments based on the method of statistical texture and the method of structural texture were carried out respectively. On the basis of qualitative analysis, quantitative analysis based on the built-up area selected artificially is enforced, in the relatively simple experimentation area, detection rate is more than 90%, in the relatively complex experimentation area, detection rate is also higher than the other two methods. In the study-area, the results show that this method can effectively and accurately extract built-up areas in high resolution airborne SAR imagery.

Modelling Inter-relationships among water, governance, human development variables in developing countries with Bayesian networks.

NASA Astrophysics Data System (ADS)

Dondeynaz, C.; Lopez-Puga, J.; Carmona-Moreno, C.

2012-04-01

Improving Water and Sanitation Services (WSS), being a complex and interdisciplinary issue, passes through collaboration and coordination of different sectors (environment, health, economic activities, governance, and international cooperation). This inter-dependency has been recognised with the adoption of the "Integrated Water Resources Management" principles that push for the integration of these various dimensions involved in WSS delivery to ensure an efficient and sustainable management. The understanding of these interrelations appears as crucial for decision makers in the water sector in particular in developing countries where WSS still represent an important leverage for livelihood improvement. In this framework, the Joint Research Centre of the European Commission has developed a coherent database (WatSan4Dev database) containing 29 indicators from environmental, socio-economic, governance and financial aid flows data focusing on developing countries (Celine et al, 2011 under publication). The aim of this work is to model the WatSan4Dev dataset using probabilistic models to identify the key variables influencing or being influenced by the water supply and sanitation access levels. Bayesian Network Models are suitable to map the conditional dependencies between variables and also allows ordering variables by level of influence on the dependent variable. Separated models have been built for water supply and for sanitation because of different behaviour. The models are validated if complying with statistical criteria but either with scientific knowledge and literature. A two steps approach has been adopted to build the structure of the model; Bayesian network is first built for each thematic cluster of variables (e.g governance, agricultural pressure, or human development) keeping a detailed level for interpretation later one. A global model is then built based on significant indicators of each cluster being previously modelled. The structure of the relationships between variable are set a priori according to literature and/or experience in the field (expert knowledge). The statistical validation is verified according to error rate of classification, and the significance of the variables. Sensibility analysis has also been performed to characterise the relative influence of every single variable in the model. Once validated, the models allow the estimation of impact of each variable on the behaviour of the water supply or sanitation providing an interesting mean to test scenarios and predict variables behaviours. The choices made, methods and description of the various models, for each cluster as well as the global model for water supply and sanitation will be presented. Key results and interpretation of the relationships depicted by the models will be detailed during the conference.

Statistical Hypothesis Testing in Intraspecific Phylogeography: NCPA versus ABC

PubMed Central

Templeton, Alan R.

2009-01-01

Nested clade phylogeographic analysis (NCPA) and approximate Bayesian computation (ABC) have been used to test phylogeographic hypotheses. Multilocus NCPA tests null hypotheses, whereas ABC discriminates among a finite set of alternatives. The interpretive criteria of NCPA are explicit and allow complex models to be built from simple components. The interpretive criteria of ABC are ad hoc and require the specification of a complete phylogeographic model. The conclusions from ABC are often influenced by implicit assumptions arising from the many parameters needed to specify a complex model. These complex models confound many assumptions so that biological interpretations are difficult. Sampling error is accounted for in NCPA, but ABC ignores important sources of sampling error that creates pseudo-statistical power. NCPA generates the full sampling distribution of its statistics, but ABC only yields local probabilities, which in turn make it impossible to distinguish between a good fitting model, a non-informative model, and an over-determined model. Both NCPA and ABC use approximations, but convergences of the approximations used in NCPA are well defined whereas those in ABC are not. NCPA can analyze a large number of locations, but ABC cannot. Finally, the dimensionality of tested hypothesis is known in NCPA, but not for ABC. As a consequence, the “probabilities” generated by ABC are not true probabilities and are statistically non-interpretable. Accordingly, ABC should not be used for hypothesis testing, but simulation approaches are valuable when used in conjunction with NCPA or other methods that do not rely on highly parameterized models. PMID:19192182

Non-linear effects of the built environment on automobile-involved pedestrian crash frequency: A machine learning approach.

PubMed

Ding, Chuan; Chen, Peng; Jiao, Junfeng

2018-03-01

Although a growing body of literature focuses on the relationship between the built environment and pedestrian crashes, limited evidence is provided about the relative importance of many built environment attributes by accounting for their mutual interaction effects and their non-linear effects on automobile-involved pedestrian crashes. This study adopts the approach of Multiple Additive Poisson Regression Trees (MAPRT) to fill such gaps using pedestrian collision data collected from Seattle, Washington. Traffic analysis zones are chosen as the analytical unit. The effects of various factors on pedestrian crash frequency investigated include characteristics the of road network, street elements, land use patterns, and traffic demand. Density and the degree of mixed land use have major effects on pedestrian crash frequency, accounting for approximately 66% of the effects in total. More importantly, some factors show clear non-linear relationships with pedestrian crash frequency, challenging the linearity assumption commonly used in existing studies which employ statistical models. With various accurately identified non-linear relationships between the built environment and pedestrian crashes, this study suggests local agencies to adopt geo-spatial differentiated policies to establish a safe walking environment. These findings, especially the effective ranges of the built environment, provide evidence to support for transport and land use planning, policy recommendations, and road safety programs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Older adults' quality of life - Exploring the role of the built environment and social cohesion in community-dwelling seniors on low income.

PubMed

Engel, L; Chudyk, A M; Ashe, M C; McKay, H A; Whitehurst, D G T; Bryan, S

2016-09-01

The built environment and social cohesion are increasingly recognized as being associated with older adults' quality of life (QoL). However, limited research in this area still exists and the relationship has remained unexplored in the area of Metro Vancouver, Canada. This study examined the association between the built environment and social cohesion with QoL of 160 community-dwelling older adults (aged ≥ 65 years) on low income from Metro Vancouver. Cross-sectional data acquired from the Walk the Talk (WTT) study were used. Health-related QoL (HRQoL) and capability wellbeing were assessed using the EQ-5D-5L and the ICECAP-O, respectively. Measures of the environment comprised the NEWS-A (perceived built environment measure), the Street Smart Walk Score (objective built environment measure), and the SC-5PT (a measure of social cohesion). The primary analysis consists of Tobit regression models to explore the associations between environmental features and HRQoL as well as capability wellbeing. Key findings indicate that after adjusting for covariates, older adults' capability wellbeing was associated with street connectivity and social cohesion, while no statistically significant associations were found between environmental factors and HRQoL. Our results should be considered as hypothesis-generating and need confirmation in a larger longitudinal study. Copyright © 2016 Elsevier Ltd. All rights reserved.

Modeling and validation of photometric characteristics of space targets oriented to space-based observation.

PubMed

Wang, Hongyuan; Zhang, Wei; Dong, Aotuo

2012-11-10

A modeling and validation method of photometric characteristics of the space target was presented in order to track and identify different satellites effectively. The background radiation characteristics models of the target were built based on blackbody radiation theory. The geometry characteristics of the target were illustrated by the surface equations based on its body coordinate system. The material characteristics of the target surface were described by a bidirectional reflectance distribution function model, which considers the character of surface Gauss statistics and microscale self-shadow and is obtained by measurement and modeling in advance. The contributing surfaces of the target to observation system were determined by coordinate transformation according to the relative position of the space-based target, the background radiation sources, and the observation platform. Then a mathematical model on photometric characteristics of the space target was built by summing reflection components of all the surfaces. Photometric characteristics simulation of the space-based target was achieved according to its given geometrical dimensions, physical parameters, and orbital parameters. Experimental validation was made based on the scale model of the satellite. The calculated results fit well with the measured results, which indicates the modeling method of photometric characteristics of the space target is correct.

EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks.

PubMed

Jenness, Samuel M; Goodreau, Steven M; Morris, Martina

2018-04-01

Package EpiModel provides tools for building, simulating, and analyzing mathematical models for the population dynamics of infectious disease transmission in R. Several classes of models are included, but the unique contribution of this software package is a general stochastic framework for modeling the spread of epidemics on networks. EpiModel integrates recent advances in statistical methods for network analysis (temporal exponential random graph models) that allow the epidemic modeling to be grounded in empirical data on contacts that can spread infection. This article provides an overview of both the modeling tools built into EpiModel , designed to facilitate learning for students new to modeling, and the application programming interface for extending package EpiModel , designed to facilitate the exploration of novel research questions for advanced modelers.

EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks

PubMed Central

Jenness, Samuel M.; Goodreau, Steven M.; Morris, Martina

2018-01-01

Package EpiModel provides tools for building, simulating, and analyzing mathematical models for the population dynamics of infectious disease transmission in R. Several classes of models are included, but the unique contribution of this software package is a general stochastic framework for modeling the spread of epidemics on networks. EpiModel integrates recent advances in statistical methods for network analysis (temporal exponential random graph models) that allow the epidemic modeling to be grounded in empirical data on contacts that can spread infection. This article provides an overview of both the modeling tools built into EpiModel, designed to facilitate learning for students new to modeling, and the application programming interface for extending package EpiModel, designed to facilitate the exploration of novel research questions for advanced modelers. PMID:29731699

"Plate cherry picking": a novel semi-sequential screening paradigm for cheaper, faster, information-rich compound selection.

PubMed

Crisman, Thomas J; Jenkins, Jeremy L; Parker, Christian N; Hill, W Adam G; Bender, Andreas; Deng, Zhan; Nettles, James H; Davies, John W; Glick, Meir

2007-04-01

This work describes a novel semi-sequential technique for in silico enhancement of high-throughput screening (HTS) experiments now employed at Novartis. It is used in situations in which the size of the screen is limited by the readout (e.g., high-content screens) or the amount of reagents or tools (proteins or cells) available. By performing computational chemical diversity selection on a per plate basis (instead of a per compound basis), 25% of the 1,000,000-compound screening was optimized for general initial HTS. Statistical models are then generated from target-specific primary results (percentage inhibition data) to drive the cherry picking and testing from the entire collection. Using retrospective analysis of 11 HTS campaigns, the authors show that this method would have captured on average two thirds of the active compounds (IC(50) < 10 microM) and three fourths of the active Murcko scaffolds while decreasing screening expenditure by nearly 75%. This result is true for a wide variety of targets, including G-protein-coupled receptors, chemokine receptors, kinases, metalloproteinases, pathway screens, and protein-protein interactions. Unlike time-consuming "classic" sequential approaches that require multiple iterations of cherry picking, testing, and building statistical models, here individual compounds are cherry picked just once, based directly on primary screening data. Strikingly, the authors demonstrate that models built from primary data are as robust as models built from IC(50) data. This is true for all HTS campaigns analyzed, which represent a wide variety of target classes and assay types.

Rasch-built Overall Disability Scale (R-ODS) for immune-mediated peripheral neuropathies.

PubMed

van Nes, S I; Vanhoutte, E K; van Doorn, P A; Hermans, M; Bakkers, M; Kuitwaard, K; Faber, C G; Merkies, I S J

2011-01-25

To develop a patient-based, linearly weighted scale that captures activity and social participation limitations in patients with Guillain-Barré syndrome (GBS), chronic inflammatory demyelinating polyradiculoneuropathy (CIDP), and gammopathy-related polyneuropathy (MGUSP). A preliminary Rasch-built Overall Disability Scale (R-ODS) containing 146 activity and participation items was constructed, based on the WHO International Classification of Functioning, Disability and Health, literature search, and patient interviews. The preliminary R-ODS was assessed twice (interval: 2-4 weeks; test-retest reliability studies) in 294 patients who experienced GBS in the past (n = 174) or currently have stable CIDP (n = 80) or MGUSP (n = 40). Data were analyzed using the Rasch unidimensional measurement model (RUMM2020). The preliminary R-ODS did not meet the Rasch model expectations. Based on disordered thresholds, misfit statistics, item bias, and local dependency, items were systematically removed to improve the model fit, regularly controlling the class intervals and model statistics. Finally, we succeeded in constructing a 24-item scale that fulfilled all Rasch requirements. "Reading a newspaper/book" and "eating" were the 2 easiest items; "standing for hours" and "running" were the most difficult ones. Good validity and reliability were obtained. The R-ODS is a linearly weighted scale that specifically captures activity and social participation limitations in patients with GBS, CIDP, and MGUSP. Compared to the Overall Disability Sum Score, the R-ODS represents a wider range of item difficulties, thereby better targeting patients with different ability levels. If responsive, the R-ODS will be valuable for future clinical trials and follow-up studies in these conditions.

Comparison of thermal signatures of a mine buried in mineral and organic soils

NASA Astrophysics Data System (ADS)

Lamorski, K.; Pregowski, Piotr; Swiderski, Waldemar; Usowicz, B.; Walczak, R. T.

2001-10-01

Values of thermal signature of a mine buried in soils, which ave different properties, were compared using mathematical- statistical modeling. There was applied a model of transport phenomena in the soil, which takes into consideration water and energy transfer. The energy transport is described using Fourier's equation. Liquid phase transport of water is calculated using Richard's model of water flow in porous medium. For the comparison, there were selected two soils: mineral and organic, which differs significantly in thermal and hydrological properties. The heat capacity of soil was estimated using de Vries model. The thermal conductivity was calculated using a statistical model, which incorprates fundamental soil physical properties. The model of soil thermal conductivity was built on the base of heat resistance, two Kirchhoff's laws and polynomial distribution. Soil hydrological properties were described using Mualem-van Genuchten model. The impact of thermal properties of the medium in which a mien had been placed on its thermal signature in the conditions of heat input was presented. The dependence was stated between observed thermal signature of a mine and thermal parameters of the medium.

Generating community-built tools for data sharing and analysis in environmental networks

USGS Publications Warehouse

Read, Jordan S.; Gries, Corinna; Read, Emily K.; Klug, Jennifer; Hanson, Paul C.; Hipsey, Matthew R.; Jennings, Eleanor; O'Reilley, Catherine; Winslow, Luke A.; Pierson, Don; McBride, Christopher G.; Hamilton, David

2016-01-01

Rapid data growth in many environmental sectors has necessitated tools to manage and analyze these data. The development of tools often lags behind the proliferation of data, however, which may slow exploratory opportunities and scientific progress. The Global Lake Ecological Observatory Network (GLEON) collaborative model supports an efficient and comprehensive data–analysis–insight life cycle, including implementations of data quality control checks, statistical calculations/derivations, models, and data visualizations. These tools are community-built and openly shared. We discuss the network structure that enables tool development and a culture of sharing, leading to optimized output from limited resources. Specifically, data sharing and a flat collaborative structure encourage the development of tools that enable scientific insights from these data. Here we provide a cross-section of scientific advances derived from global-scale analyses in GLEON. We document enhancements to science capabilities made possible by the development of analytical tools and highlight opportunities to expand this framework to benefit other environmental networks.

Selection of appropriate training and validation set chemicals for modelling dermal permeability by U-optimal design.

PubMed

Xu, G; Hughes-Oliver, J M; Brooks, J D; Yeatts, J L; Baynes, R E

2013-01-01

Quantitative structure-activity relationship (QSAR) models are being used increasingly in skin permeation studies. The main idea of QSAR modelling is to quantify the relationship between biological activities and chemical properties, and thus to predict the activity of chemical solutes. As a key step, the selection of a representative and structurally diverse training set is critical to the prediction power of a QSAR model. Early QSAR models selected training sets in a subjective way and solutes in the training set were relatively homogenous. More recently, statistical methods such as D-optimal design or space-filling design have been applied but such methods are not always ideal. This paper describes a comprehensive procedure to select training sets from a large candidate set of 4534 solutes. A newly proposed 'Baynes' rule', which is a modification of Lipinski's 'rule of five', was used to screen out solutes that were not qualified for the study. U-optimality was used as the selection criterion. A principal component analysis showed that the selected training set was representative of the chemical space. Gas chromatograph amenability was verified. A model built using the training set was shown to have greater predictive power than a model built using a previous dataset [1].

A model of strength

USGS Publications Warehouse

Johnson, Douglas H.; Cook, R.D.

2013-01-01

In her AAAS News & Notes piece "Can the Southwest manage its thirst?" (26 July, p. 362), K. Wren quotes Ajay Kalra, who advocates a particular method for predicting Colorado River streamflow "because it eschews complex physical climate models for a statistical data-driven modeling approach." A preference for data-driven models may be appropriate in this individual situation, but it is not so generally, Data-driven models often come with a warning against extrapolating beyond the range of the data used to develop the models. When the future is like the past, data-driven models can work well for prediction, but it is easy to over-model local or transient phenomena, often leading to predictive inaccuracy (1). Mechanistic models are built on established knowledge of the process that connects the response variables with the predictors, using information obtained outside of an extant data set. One may shy away from a mechanistic approach when the underlying process is judged to be too complicated, but good predictive models can be constructed with statistical components that account for ingredients missing in the mechanistic analysis. Models with sound mechanistic components are more generally applicable and robust than data-driven models.

Expected social utility of life time in the presence of a chronic disease.

PubMed

Mulder, P G; Hempenius, A L

1993-10-01

Interventive action aimed at reducing the incidence of an irreversible chronic noncommunicable disease in a population has various effects. Hopefully, it increases total longevity in the population and it causes the disease to develop later in time in a smaller portion of the population. In this paper a statistical model is built by which these effects can be estimated. A three dimensional probability density function that underlies this model is changed by the interventive action. It is shown how a three dimensional utility function can be defined to appropriately judge this change.

Non-parametric model selection for subject-specific topological organization of resting-state functional connectivity.

PubMed

Ferrarini, Luca; Veer, Ilya M; van Lew, Baldur; Oei, Nicole Y L; van Buchem, Mark A; Reiber, Johan H C; Rombouts, Serge A R B; Milles, J

2011-06-01

In recent years, graph theory has been successfully applied to study functional and anatomical connectivity networks in the human brain. Most of these networks have shown small-world topological characteristics: high efficiency in long distance communication between nodes, combined with highly interconnected local clusters of nodes. Moreover, functional studies performed at high resolutions have presented convincing evidence that resting-state functional connectivity networks exhibits (exponentially truncated) scale-free behavior. Such evidence, however, was mostly presented qualitatively, in terms of linear regressions of the degree distributions on log-log plots. Even when quantitative measures were given, these were usually limited to the r(2) correlation coefficient. However, the r(2) statistic is not an optimal estimator of explained variance, when dealing with (truncated) power-law models. Recent developments in statistics have introduced new non-parametric approaches, based on the Kolmogorov-Smirnov test, for the problem of model selection. In this work, we have built on this idea to statistically tackle the issue of model selection for the degree distribution of functional connectivity at rest. The analysis, performed at voxel level and in a subject-specific fashion, confirmed the superiority of a truncated power-law model, showing high consistency across subjects. Moreover, the most highly connected voxels were found to be consistently part of the default mode network. Our results provide statistically sound support to the evidence previously presented in literature for a truncated power-law model of resting-state functional connectivity. Copyright © 2010 Elsevier Inc. All rights reserved.

Nuclear reactors built, being built, or planned 1993

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1993-08-01

Nuclear Reactors Built, Being Built, or Planned contains unclassified information about facilities built, being built, or planned in the United States for domestic use or export as of December 31, 1993. The Office of Scientific and Technical Information, US Department of Energy, gathers this information annually from Washington headquarters and field offices of DOE; from the US Nuclear Regulatory Commission (NRC); from the US reactor manufacturers who are the principal nuclear embassies; and from foreign governmental nuclear departments. The book consists of three divisions, as follows: (1) a commercial reactor locator map and tables of the characteristic and statistical datamore » that follow; a table of abbreviations; (2) tables of data for reactors operating, being built, or planned; and (3) tables of data for reactors that have been shut down permanently or dismantled. The reactors are subdivided into the following parts: civilian, production, military, export, and critical assembly.« less

New auto-segment method of cerebral hemorrhage

NASA Astrophysics Data System (ADS)

Wang, Weijiang; Shen, Tingzhi; Dang, Hua

2007-12-01

A novel method for Computerized tomography (CT) cerebral hemorrhage (CH) image automatic segmentation is presented in the paper, which uses expert system that models human knowledge about the CH automatic segmentation problem. The algorithm adopts a series of special steps and extracts some easy ignored CH features which can be found by statistic results of mass real CH images, such as region area, region CT number, region smoothness and some statistic CH region relationship. And a seven steps' extracting mechanism will ensure these CH features can be got correctly and efficiently. By using these CH features, a decision tree which models the human knowledge about the CH automatic segmentation problem has been built and it will ensure the rationality and accuracy of the algorithm. Finally some experiments has been taken to verify the correctness and reasonable of the automatic segmentation, and the good correct ratio and fast speed make it possible to be widely applied into practice.

On-demand Reporting of Risk-adjusted and Smoothed Rates for Quality Profiling in ACS NSQIP.

PubMed

Cohen, Mark E; Liu, Yaoming; Huffman, Kristopher M; Ko, Clifford Y; Hall, Bruce L

2016-12-01

Surgical quality improvement depends on hospitals having accurate and timely information about comparative performance. Profiling accuracy is improved by risk adjustment and shrinkage adjustment to stabilize estimates. These adjustments are included in ACS NSQIP reports, where hospital odds ratios (OR) are estimated using hierarchical models built on contemporaneous data. However, the timeliness of feedback remains an issue. We describe an alternative, nonhierarchical approach, which yields risk- and shrinkage-adjusted rates. In contrast to our "Traditional" NSQIP method, this approach uses preexisting equations, built on historical data, which permits hospitals to have near immediate access to profiling results. We compared our traditional method to this new "on-demand" approach with respect to outlier determinations, kappa statistics, and correlations between logged OR and standardized rates, for 12 models (4 surgical groups by 3 outcomes). When both methods used the same contemporaneous data, there were similar numbers of hospital outliers and correlations between logged OR and standardized rates were high. However, larger differences were observed when the effect of contemporaneous versus historical data was added to differences in statistical methodology. The on-demand, nonhierarchical approach provides results similar to the traditional hierarchical method and offers immediacy, an "over-time" perspective, application to a broader range of models and data subsets, and reporting of more easily understood rates. Although the nonhierarchical method results are now available "on-demand" in a web-based application, the hierarchical approach has advantages, which support its continued periodic publication as the gold standard for hospital profiling in the program.

A Constructivist Approach in a Blended E-Learning Environment for Statistics

ERIC Educational Resources Information Center

Poelmans, Stephan; Wessa, Patrick

2015-01-01

In this study, we report on the students' evaluation of a self-constructed constructivist e-learning environment for statistics, the compendium platform (CP). The system was built to endorse deeper learning with the incorporation of statistical reproducibility and peer review practices. The deployment of the CP, with interactive workshops and…

Interactions of donor sources and media influence the histo-morphological quality of full-thickness skin models.

PubMed

Lange, Julia; Weil, Frederik; Riegler, Christoph; Groeber, Florian; Rebhan, Silke; Kurdyn, Szymon; Alb, Miriam; Kneitz, Hermann; Gelbrich, Götz; Walles, Heike; Mielke, Stephan

2016-10-01

Human artificial skin models are increasingly employed as non-animal test platforms for research and medical purposes. However, the overall histopathological quality of such models may vary significantly. Therefore, the effects of manufacturing protocols and donor sources on the quality of skin models built-up from fibroblasts and keratinocytes derived from juvenile foreskins is studied. Histo-morphological parameters such as epidermal thickness, number of epidermal cell layers, dermal thickness, dermo-epidermal adhesion and absence of cellular nuclei in the corneal layer are obtained and scored accordingly. In total, 144 full-thickness skin models derived from 16 different donors, built-up in triplicates using three different culture conditions were successfully generated. In univariate analysis both media and donor age affected the quality of skin models significantly. Both parameters remained statistically significant in multivariate analyses. Performing general linear model analyses we could show that individual medium-donor-interactions influence the quality. These observations suggest that the optimal choice of media may differ from donor to donor and coincides with findings where significant inter-individual variations of growth rates in keratinocytes and fibroblasts have been described. Thus, the consideration of individual medium-donor-interactions may improve the overall quality of human organ models thereby forming a reproducible test platform for sophisticated clinical research. Copyright © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Due Date Assignment in a Dynamic Job Shop with the Orthogonal Kernel Least Squares Algorithm

NASA Astrophysics Data System (ADS)

Yang, D. H.; Hu, L.; Qian, Y.

2017-06-01

Meeting due dates is a key goal in the manufacturing industries. This paper proposes a method for due date assignment (DDA) by using the Orthogonal Kernel Least Squares Algorithm (OKLSA). A simulation model is built to imitate the production process of a highly dynamic job shop. Several factors describing job characteristics and system state are extracted as attributes to predict job flow-times. A number of experiments under conditions of varying dispatching rules and 90% shop utilization level have been carried out to evaluate the effectiveness of OKLSA applied for DDA. The prediction performance of OKLSA is compared with those of five conventional DDA models and back-propagation neural network (BPNN). The experimental results indicate that OKLSA is statistically superior to other DDA models in terms of mean absolute lateness and root mean squares lateness in most cases. The only exception occurs when the shortest processing time rule is used for dispatching jobs, the difference between OKLSA and BPNN is not statistically significant.

Applying quantitative adiposity feature analysis models to predict benefit of bevacizumab-based chemotherapy in ovarian cancer patients

NASA Astrophysics Data System (ADS)

Wang, Yunzhi; Qiu, Yuchen; Thai, Theresa; More, Kathleen; Ding, Kai; Liu, Hong; Zheng, Bin

2016-03-01

How to rationally identify epithelial ovarian cancer (EOC) patients who will benefit from bevacizumab or other antiangiogenic therapies is a critical issue in EOC treatments. The motivation of this study is to quantitatively measure adiposity features from CT images and investigate the feasibility of predicting potential benefit of EOC patients with or without receiving bevacizumab-based chemotherapy treatment using multivariate statistical models built based on quantitative adiposity image features. A dataset involving CT images from 59 advanced EOC patients were included. Among them, 32 patients received maintenance bevacizumab after primary chemotherapy and the remaining 27 patients did not. We developed a computer-aided detection (CAD) scheme to automatically segment subcutaneous fat areas (VFA) and visceral fat areas (SFA) and then extracted 7 adiposity-related quantitative features. Three multivariate data analysis models (linear regression, logistic regression and Cox proportional hazards regression) were performed respectively to investigate the potential association between the model-generated prediction results and the patients' progression-free survival (PFS) and overall survival (OS). The results show that using all 3 statistical models, a statistically significant association was detected between the model-generated results and both of the two clinical outcomes in the group of patients receiving maintenance bevacizumab (p<0.01), while there were no significant association for both PFS and OS in the group of patients without receiving maintenance bevacizumab. Therefore, this study demonstrated the feasibility of using quantitative adiposity-related CT image features based statistical prediction models to generate a new clinical marker and predict the clinical outcome of EOC patients receiving maintenance bevacizumab-based chemotherapy.

Modelling seasonal effects of temperature and precipitation on honey bee winter mortality in a temperate climate.

PubMed

Switanek, Matthew; Crailsheim, Karl; Truhetz, Heimo; Brodschneider, Robert

2017-02-01

Insect pollinators are essential to global food production. For this reason, it is alarming that honey bee (Apis mellifera) populations across the world have recently seen increased rates of mortality. These changes in colony mortality are often ascribed to one or more factors including parasites, diseases, pesticides, nutrition, habitat dynamics, weather and/or climate. However, the effect of climate on colony mortality has never been demonstrated. Therefore, in this study, we focus on longer-term weather conditions and/or climate's influence on honey bee winter mortality rates across Austria. Statistical correlations between monthly climate variables and winter mortality rates were investigated. Our results indicate that warmer and drier weather conditions in the preceding year were accompanied by increased winter mortality. We subsequently built a statistical model to predict colony mortality using temperature and precipitation data as predictors. Our model reduces the mean absolute error between predicted and observed colony mortalities by 9% and is statistically significant at the 99.9% confidence level. This is the first study to show clear evidence of a link between climate variability and honey bee winter mortality. Copyright © 2016 British Geological Survey, NERC. Published by Elsevier B.V. All rights reserved.

Microstructure development in Kolmogorov, Johnson-Mehl, and Avrami nucleation and growth kinetics

NASA Astrophysics Data System (ADS)

Pineda, Eloi; Crespo, Daniel

1999-08-01

A statistical model with the ability to evaluate the microstructure developed in nucleation and growth kinetics is built in the framework of the Kolmogorov, Johnson-Mehl, and Avrami theory. A populational approach is used to compute the observed grain-size distribution. The impingement process which delays grain growth is analyzed, and the effective growth rate of each population is estimated considering the previous grain history. The proposed model is integrated for a wide range of nucleation and growth protocols, including constant nucleation, pre-existing nuclei, and intermittent nucleation with interface or diffusion-controlled grain growth. The results are compared with Monte Carlo simulations, giving quantitative agreement even in cases where previous models fail.

Contrail Tracking and ARM Data Product Development

NASA Technical Reports Server (NTRS)

Duda, David P.; Russell, James, III

2005-01-01

A contrail tracking system was developed to help in the assessment of the effect of commercial jet contrails on the Earth's radiative budget. The tracking system was built by combining meteorological data from the Rapid Update Cycle (RUC) numerical weather prediction model with commercial air traffic flight track data and satellite imagery. A statistical contrail-forecasting model was created a combination of surface-based contrail observations and numerical weather analyses and forecasts. This model allows predictions of widespread contrail occurrences for contrail research on either a real-time basis or for long-term time scales. Satellite-derived cirrus cloud properties in polluted and unpolluted regions were compared to determine the impact of air traffic on cirrus.

Epidemic modeling with discrete-space scheduled walkers: extensions and research opportunities

PubMed Central

2009-01-01

Background This exploratory paper outlines an epidemic simulator built on an agent-based, data-driven model of the spread of a disease within an urban environment. An intent of the model is to provide insight into how a disease may reach a tipping point, spreading to an epidemic of uncontrollable proportions. Methods As a complement to analytical methods, simulation is arguably an effective means of gaining a better understanding of system-level disease dynamics within a population and offers greater utility in its modeling capabilities. Our investigation is based on this conjecture, supported by data-driven models that are reasonable, realistic and practical, in an attempt to demonstrate their efficacy in studying system-wide epidemic phenomena. An agent-based model (ABM) offers considerable flexibility in extending the study of the phenomena before, during and after an outbreak or catastrophe. Results An agent-based model was developed based on a paradigm of a 'discrete-space scheduled walker' (DSSW), modeling a medium-sized North American City of 650,000 discrete agents, built upon a conceptual framework of statistical reasoning (law of large numbers, statistical mechanics) as well as a correct-by-construction bias. The model addresses where, who, when and what elements, corresponding to network topography and agent characteristics, behaviours, and interactions upon that topography. The DSSW-ABM has an interface and associated scripts that allow for a variety of what-if scenarios modeling disease spread throughout the population, and for data to be collected and displayed via a web browser. Conclusion This exploratory paper also presents several research opportunities for exploiting data sources of a non-obvious and disparate nature for the purposes of epidemic modeling. There is an increasing amount and variety of data that will continue to contribute to the accuracy of agent-based models and improve their utility in modeling disease spread. The model developed here is well suited to diseases where there is not a predisposition for contraction within the population. One of the advantages of agent-based modeling is the ability to set up a rare event and develop policy as to how one may mitigate damages arising from it. PMID:19922684

Epidemic modeling with discrete-space scheduled walkers: extensions and research opportunities.

PubMed

Borkowski, Maciej; Podaima, Blake W; McLeod, Robert D

2009-11-18

This exploratory paper outlines an epidemic simulator built on an agent-based, data-driven model of the spread of a disease within an urban environment. An intent of the model is to provide insight into how a disease may reach a tipping point, spreading to an epidemic of uncontrollable proportions. As a complement to analytical methods, simulation is arguably an effective means of gaining a better understanding of system-level disease dynamics within a population and offers greater utility in its modeling capabilities. Our investigation is based on this conjecture, supported by data-driven models that are reasonable, realistic and practical, in an attempt to demonstrate their efficacy in studying system-wide epidemic phenomena. An agent-based model (ABM) offers considerable flexibility in extending the study of the phenomena before, during and after an outbreak or catastrophe. An agent-based model was developed based on a paradigm of a 'discrete-space scheduled walker' (DSSW), modeling a medium-sized North American City of 650,000 discrete agents, built upon a conceptual framework of statistical reasoning (law of large numbers, statistical mechanics) as well as a correct-by-construction bias. The model addresses where, who, when and what elements, corresponding to network topography and agent characteristics, behaviours, and interactions upon that topography. The DSSW-ABM has an interface and associated scripts that allow for a variety of what-if scenarios modeling disease spread throughout the population, and for data to be collected and displayed via a web browser. This exploratory paper also presents several research opportunities for exploiting data sources of a non-obvious and disparate nature for the purposes of epidemic modeling. There is an increasing amount and variety of data that will continue to contribute to the accuracy of agent-based models and improve their utility in modeling disease spread. The model developed here is well suited to diseases where there is not a predisposition for contraction within the population. One of the advantages of agent-based modeling is the ability to set up a rare event and develop policy as to how one may mitigate damages arising from it.

Polar semiconductor heterojunction structure energy band diagram considerations

NASA Astrophysics Data System (ADS)

Lin, Shuxun; Wen, Cheng P.; Wang, Maojun; Hao, Yilong

2016-03-01

The unique nature of built-in electric field induced positive/negative charge pairs of polar semiconductor heterojunction structure has led to a more realistic device model for hexagonal III-nitride HEMT. In this modeling approach, the distribution of charge carriers is dictated by the electrostatic potential profile instead of Femi statistics. The proposed device model is found suitable to explain peculiar properties of GaN HEMT structures, including: (1) Discrepancy in measured conventional linear transmission line model (LTLM) sheet resistance and contactless sheet resistance of GaN HEMT with thin barrier layer. (2) Below bandgap radiation from forward biased Nickel Schottky barrier diode on GaN HEMT structure. (3) GaN HEMT barrier layer doping has negligible effect on transistor channel sheet charge density.

An issue of literacy on pediatric arterial hypertension

NASA Astrophysics Data System (ADS)

Teodoro, M. Filomena; Romana, Andreia; Simão, Carla

2017-11-01

Arterial hypertension in pediatric age is a public health problem, whose prevalence has increased significantly over time. Pediatric arterial hypertension (PAH) is under-diagnosed in most cases, a highly prevalent disease, appears without notice with multiple consequences on the children's health and future adults. Children caregivers and close family must know the PAH existence, the negative consequences associated with it, the risk factors and, finally, must do prevention. In [12, 13] can be found a statistical data analysis using a simpler questionnaire introduced in [4] under the aim of a preliminary study about PAH caregivers acquaintance. A continuation of such analysis is detailed in [14]. An extension of such questionnaire was built and applied to a distinct population and it was filled online. The statistical approach is partially reproduced in the present work. Some statistical models were estimated using several approaches, namely multivariate analysis (factorial analysis), also adequate methods to analyze the kind of data in study.

PSSMSearch: a server for modeling, visualization, proteome-wide discovery and annotation of protein motif specificity determinants.

PubMed

Krystkowiak, Izabella; Manguy, Jean; Davey, Norman E

2018-06-05

There is a pressing need for in silico tools that can aid in the identification of the complete repertoire of protein binding (SLiMs, MoRFs, miniMotifs) and modification (moiety attachment/removal, isomerization, cleavage) motifs. We have created PSSMSearch, an interactive web-based tool for rapid statistical modeling, visualization, discovery and annotation of protein motif specificity determinants to discover novel motifs in a proteome-wide manner. PSSMSearch analyses proteomes for regions with significant similarity to a motif specificity determinant model built from a set of aligned motif-containing peptides. Multiple scoring methods are available to build a position-specific scoring matrix (PSSM) describing the motif specificity determinant model. This model can then be modified by a user to add prior knowledge of specificity determinants through an interactive PSSM heatmap. PSSMSearch includes a statistical framework to calculate the significance of specificity determinant model matches against a proteome of interest. PSSMSearch also includes the SLiMSearch framework's annotation, motif functional analysis and filtering tools to highlight relevant discriminatory information. Additional tools to annotate statistically significant shared keywords and GO terms, or experimental evidence of interaction with a motif-recognizing protein have been added. Finally, PSSM-based conservation metrics have been created for taxonomic range analyses. The PSSMSearch web server is available at http://slim.ucd.ie/pssmsearch/.

[Introduction and advantage analysis of the stepwise method for the construction of vascular trees].

PubMed

Zhang, Yan; Xie, Haiwei; Zhu, Kai

2010-08-01

A new method for constructing the model of vascular trees was proposed in this paper. By use of this method, the arterial trees in good agreement with the actual structure could be grown. In this process, all vessels in the vascular tree were divided into two groups: the conveying vessels, and the delivering branches. And different branches could be built by different ways. Firstly, the distributing rules of conveying vessels were ascertained by use of measurement data, and then the conveying vessels were constructed in accordance to the statistical rule and optimization criterion. Lastly, delivering branches were modeled by constrained constructive optimization (CCO) on the conveying vessel-trees which had already been generated. In order to compare the CCO method and stepwise method proposed here, two 3D arterial trees of human tongue were grown with their vascular tree having a special structure. Based on the corrosion casts of real arterial tree of human tongue, the data about the two trees constructed by different methods were compared and analyzed, including the averaged segment diameters at respective levels, the distribution and the diameters of the branches of first level at respective directions. The results show that the vascular tree built by stepwise method is more similar to the true arterial of human tongue when compared against the tree built by CCO method.

Preliminary Study on Appearance-Based Detection of Anatomical Point Landmarks in Body Trunk CT Images

NASA Astrophysics Data System (ADS)

Nemoto, Mitsutaka; Nomura, Yukihiro; Hanaoka, Shohei; Masutani, Yoshitaka; Yoshikawa, Takeharu; Hayashi, Naoto; Yoshioka, Naoki; Ohtomo, Kuni

Anatomical point landmarks as most primitive anatomical knowledge are useful for medical image understanding. In this study, we propose a detection method for anatomical point landmark based on appearance models, which include gray-level statistical variations at point landmarks and their surrounding area. The models are built based on results of Principal Component Analysis (PCA) of sample data sets. In addition, we employed generative learning method by transforming ROI of sample data. In this study, we evaluated our method with 24 data sets of body trunk CT images and obtained 95.8 ± 7.3 % of the average sensitivity in 28 landmarks.

QSAR modeling for anti-human African trypanosomiasis activity of substituted 2-Phenylimidazopyridines

NASA Astrophysics Data System (ADS)

Masand, Vijay H.; El-Sayed, Nahed N. E.; Mahajan, Devidas T.; Mercader, Andrew G.; Alafeefy, Ahmed M.; Shibi, I. G.

2017-02-01

In the present work, sixty substituted 2-Phenylimidazopyridines previously reported with potent anti-human African trypanosomiasis (HAT) activity were selected to build genetic algorithm (GA) based QSAR models to determine the structural features that have significant correlation with the activity. Multiple QSAR models were built using easily interpretable descriptors that are directly associated with the presence or the absence of a structural scaffold, or a specific atom. All the QSAR models have been thoroughly validated according to the OECD principles. All the QSAR models are statistically very robust (R2 = 0.80-0.87) with high external predictive ability (CCCex = 0.81-0.92). The QSAR analysis reveals that the HAT activity has good correlation with the presence of five membered rings in the molecule.

Assessing risk factors for periodontitis using regression

NASA Astrophysics Data System (ADS)

Lobo Pereira, J. A.; Ferreira, Maria Cristina; Oliveira, Teresa

2013-10-01

Multivariate statistical analysis is indispensable to assess the associations and interactions between different factors and the risk of periodontitis. Among others, regression analysis is a statistical technique widely used in healthcare to investigate and model the relationship between variables. In our work we study the impact of socio-demographic, medical and behavioral factors on periodontal health. Using regression, linear and logistic models, we can assess the relevance, as risk factors for periodontitis disease, of the following independent variables (IVs): Age, Gender, Diabetic Status, Education, Smoking status and Plaque Index. The multiple linear regression analysis model was built to evaluate the influence of IVs on mean Attachment Loss (AL). Thus, the regression coefficients along with respective p-values will be obtained as well as the respective p-values from the significance tests. The classification of a case (individual) adopted in the logistic model was the extent of the destruction of periodontal tissues defined by an Attachment Loss greater than or equal to 4 mm in 25% (AL≥4mm/≥25%) of sites surveyed. The association measures include the Odds Ratios together with the correspondent 95% confidence intervals.

Optimizing DNA assembly based on statistical language modelling.

PubMed

Fang, Gang; Zhang, Shemin; Dong, Yafei

2017-12-15

By successively assembling genetic parts such as BioBrick according to grammatical models, complex genetic constructs composed of dozens of functional blocks can be built. However, usually every category of genetic parts includes a few or many parts. With increasing quantity of genetic parts, the process of assembling more than a few sets of these parts can be expensive, time consuming and error prone. At the last step of assembling it is somewhat difficult to decide which part should be selected. Based on statistical language model, which is a probability distribution P(s) over strings S that attempts to reflect how frequently a string S occurs as a sentence, the most commonly used parts will be selected. Then, a dynamic programming algorithm was designed to figure out the solution of maximum probability. The algorithm optimizes the results of a genetic design based on a grammatical model and finds an optimal solution. In this way, redundant operations can be reduced and the time and cost required for conducting biological experiments can be minimized. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Uniting Cheminformatics and Chemical Theory To Predict the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules

PubMed Central

2014-01-01

We present four models of solution free-energy prediction for druglike molecules utilizing cheminformatics descriptors and theoretically calculated thermodynamic values. We make predictions of solution free energy using physics-based theory alone and using machine learning/quantitative structure–property relationship (QSPR) models. We also develop machine learning models where the theoretical energies and cheminformatics descriptors are used as combined input. These models are used to predict solvation free energy. While direct theoretical calculation does not give accurate results in this approach, machine learning is able to give predictions with a root mean squared error (RMSE) of ∼1.1 log S units in a 10-fold cross-validation for our Drug-Like-Solubility-100 (DLS-100) dataset of 100 druglike molecules. We find that a model built using energy terms from our theoretical methodology as descriptors is marginally less predictive than one built on Chemistry Development Kit (CDK) descriptors. Combining both sets of descriptors allows a further but very modest improvement in the predictions. However, in some cases, this is a statistically significant enhancement. These results suggest that there is little complementarity between the chemical information provided by these two sets of descriptors, despite their different sources and methods of calculation. Our machine learning models are also able to predict the well-known Solubility Challenge dataset with an RMSE value of 0.9–1.0 log S units. PMID:24564264

Predictor characteristics necessary for building a clinically useful risk prediction model: a simulation study.

PubMed

Schummers, Laura; Himes, Katherine P; Bodnar, Lisa M; Hutcheon, Jennifer A

2016-09-21

Compelled by the intuitive appeal of predicting each individual patient's risk of an outcome, there is a growing interest in risk prediction models. While the statistical methods used to build prediction models are increasingly well understood, the literature offers little insight to researchers seeking to gauge a priori whether a prediction model is likely to perform well for their particular research question. The objective of this study was to inform the development of new risk prediction models by evaluating model performance under a wide range of predictor characteristics. Data from all births to overweight or obese women in British Columbia, Canada from 2004 to 2012 (n = 75,225) were used to build a risk prediction model for preeclampsia. The data were then augmented with simulated predictors of the outcome with pre-set prevalence values and univariable odds ratios. We built 120 risk prediction models that included known demographic and clinical predictors, and one, three, or five of the simulated variables. Finally, we evaluated standard model performance criteria (discrimination, risk stratification capacity, calibration, and Nagelkerke's r 2 ) for each model. Findings from our models built with simulated predictors demonstrated the predictor characteristics required for a risk prediction model to adequately discriminate cases from non-cases and to adequately classify patients into clinically distinct risk groups. Several predictor characteristics can yield well performing risk prediction models; however, these characteristics are not typical of predictor-outcome relationships in many population-based or clinical data sets. Novel predictors must be both strongly associated with the outcome and prevalent in the population to be useful for clinical prediction modeling (e.g., one predictor with prevalence ≥20 % and odds ratio ≥8, or 3 predictors with prevalence ≥10 % and odds ratios ≥4). Area under the receiver operating characteristic curve values of >0.8 were necessary to achieve reasonable risk stratification capacity. Our findings provide a guide for researchers to estimate the expected performance of a prediction model before a model has been built based on the characteristics of available predictors.

A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks.

PubMed

Mujalli, Randa Oqab; de Oña, Juan

2011-10-01

This study describes a method for reducing the number of variables frequently considered in modeling the severity of traffic accidents. The method's efficiency is assessed by constructing Bayesian networks (BN). It is based on a two stage selection process. Several variable selection algorithms, commonly used in data mining, are applied in order to select subsets of variables. BNs are built using the selected subsets and their performance is compared with the original BN (with all the variables) using five indicators. The BNs that improve the indicators' values are further analyzed for identifying the most significant variables (accident type, age, atmospheric factors, gender, lighting, number of injured, and occupant involved). A new BN is built using these variables, where the results of the indicators indicate, in most of the cases, a statistically significant improvement with respect to the original BN. It is possible to reduce the number of variables used to model traffic accidents injury severity through BNs without reducing the performance of the model. The study provides the safety analysts a methodology that could be used to minimize the number of variables used in order to determine efficiently the injury severity of traffic accidents without reducing the performance of the model. Copyright © 2011 Elsevier Ltd. All rights reserved.

Imperfect pitch: Gabor's uncertainty principle and the pitch of extremely brief sounds.

PubMed

Hsieh, I-Hui; Saberi, Kourosh

2016-02-01

How brief must a sound be before its pitch is no longer perceived? The uncertainty tradeoff between temporal and spectral resolution (Gabor's principle) limits the minimum duration required for accurate pitch identification or discrimination. Prior studies have reported that pitch can be extracted from sinusoidal pulses as brief as half a cycle. This finding has been used in a number of classic papers to develop models of pitch encoding. We have found that phase randomization, which eliminates timbre confounds, degrades this ability to chance, raising serious concerns over the foundation on which classic pitch models have been built. The current study investigated whether subthreshold pitch cues may still exist in partial-cycle pulses revealed through statistical integration in a time series containing multiple pulses. To this end, we measured frequency-discrimination thresholds in a two-interval forced-choice task for trains of partial-cycle random-phase tone pulses. We found that residual pitch cues exist in these pulses but discriminating them requires an order of magnitude (ten times) larger frequency difference than that reported previously, necessitating a re-evaluation of pitch models built on earlier findings. We also found that as pulse duration is decreased to less than two cycles its pitch becomes biased toward higher frequencies, consistent with predictions of an auto-correlation model of pitch extraction.

Statistical modeling of an integrated boiler for coal fired thermal power plant.

PubMed

Chandrasekharan, Sreepradha; Panda, Rames Chandra; Swaminathan, Bhuvaneswari Natrajan

2017-06-01

The coal fired thermal power plants plays major role in the power production in the world as they are available in abundance. Many of the existing power plants are based on the subcritical technology which can produce power with the efficiency of around 33%. But the newer plants are built on either supercritical or ultra-supercritical technology whose efficiency can be up to 50%. Main objective of the work is to enhance the efficiency of the existing subcritical power plants to compensate for the increasing demand. For achieving the objective, the statistical modeling of the boiler units such as economizer, drum and the superheater are initially carried out. The effectiveness of the developed models is tested using analysis methods like R 2 analysis and ANOVA (Analysis of Variance). The dependability of the process variable (temperature) on different manipulated variables is analyzed in the paper. Validations of the model are provided with their error analysis. Response surface methodology (RSM) supported by DOE (design of experiments) are implemented to optimize the operating parameters. Individual models along with the integrated model are used to study and design the predictive control of the coal-fired thermal power plant.

Rasch-built Overall Disability Scale for patients with chemotherapy-induced peripheral neuropathy (CIPN-R-ODS).

PubMed

Binda, D; Vanhoutte, E K; Cavaletti, G; Cornblath, D R; Postma, T J; Frigeni, B; Alberti, P; Bruna, J; Velasco, R; Argyriou, A A; Kalofonos, H P; Psimaras, D; Ricard, D; Pace, A; Galiè, E; Briani, C; Dalla Torre, C; Lalisang, R I; Boogerd, W; Brandsma, D; Koeppen, S; Hense, J; Storey, D; Kerrigan, S; Schenone, A; Fabbri, S; Rossi, E; Valsecchi, M G; Faber, C G; Merkies, I S J; Galimberti, S; Lanzani, F; Mattavelli, L; Piatti, M L; Bidoli, P; Cazzaniga, M; Cortinovis, D; Lucchetta, M; Campagnolo, M; Bakkers, M; Brouwer, B; Boogerd, W; Grant, R; Reni, L; Piras, B; Pessino, A; Padua, L; Granata, G; Leandri, M; Ghignotti, I; Plasmati, R; Pastorelli, F; Heimans, J J; Eurelings, M; Meijer, R J; Grisold, W; Lindeck Pozza, E; Mazzeo, A; Toscano, A; Russo, M; Tomasello, C; Altavilla, G; Penas Prado, M; Dominguez Gonzalez, C; Dorsey, S G

2013-09-01

Chemotherapy-induced peripheral neuropathy (CIPN) is a common neurological side-effect of cancer treatment and may lead to declines in patients' daily functioning and quality of life. To date, there are no modern clinimetrically well-evaluated outcome measures available to assess disability in CIPN patients. The objective of the study was to develop an interval-weighted scale to capture activity limitations and participation restrictions in CIPN patients using the Rasch methodology and to determine its validity and reliability properties. A preliminary Rasch-built Overall Disability Scale (pre-R-ODS) comprising 146 items was assessed twice (interval: 2-3 weeks; test-retest reliability) in 281 CIPN patients with a stable clinical condition. The obtained data were subjected to Rasch analyses to determine whether model expectations would be met, and if necessarily, adaptations were made to obtain proper model fit (internal validity). External validity was obtained by correlating the CIPN-R-ODS with the National Cancer Institute-Common Toxicity Criteria (NCI-CTC) neuropathy scales and the Pain-Intensity Numeric-Rating-Scale (PI-NRS). The preliminary R-ODS did not meet Rasch model's expectations. Items displaying misfit statistics, disordered thresholds, item bias or local dependency were systematically removed. The final CIPN-R-ODS consisting of 28 items fulfilled all the model's expectations with proper validity and reliability, and was unidimensional. The final CIPN-R-ODS is a Rasch-built disease-specific, interval measure suitable to detect disability in CIPN patients and bypasses the shortcomings of classical test theory ordinal-based measures. Its use is recommended in future clinical trials in CIPN. Copyright © 2013 Elsevier Ltd. All rights reserved.

Could strength of exposure to the residential neighbourhood modify associations between walkability and physical activity?

PubMed

Ivory, Vivienne C; Blakely, Tony; Pearce, Jamie; Witten, Karen; Bagheri, Nasser; Badland, Hannah; Schofield, Grant

2015-12-01

The importance of neighbourhoods for health and wellbeing may vary according to an individual's reliance on their local resources, but this assertion is rarely tested. We investigate whether greater neighbourhood 'exposure' through reliance on or engagement with the residential setting magnifies neighbourhood-health associations. Three built environment characteristics (destination density, streetscape (attractiveness of built environment) and street connectivity) and two physical activity components (weekday and weekend accelerometer counts) were measured for 2033 residents living in 48 neighbourhoods within four New Zealand cities in 2009-2010, giving six different built environment-physical activity associations. Interactions for each built environment-physical activity association with four individual-level characteristics (acting as proxies for exposure: gender, working status, car access, and income) were assessed with multi-level regression models; a total of 24 'tests'. Of the 12 weekday built environment-physical activity tests, 5 interaction terms were significant (p < 0.05) in the expected direction (e.g. stronger streetscape-physical activity among those with restricted car access). For weekend tests, one association was statistically significant. No significant tests were contradictory. Pooled across the 12 weekday physical activity 'tests', a 1 standard deviation increase in the walkability of the built environment was associated with an overall 3.8% (95% CI: 3.6%-4.1%) greater increase in weekday physical activity across all the types of people we hypothesised to spend more time in their residential neighbourhood, and for weekend physical activity it was 4.2% (95% CI 3.9%-4.5%). Using multiple evaluation methods, interactions were in line with our hypothesis, with a stronger association seen for proxy exposure indicators (for example, restricted car access). Added to the wider evidence base, our study strengthens causal evidence of an effect of the built environment on physical activity, and highlights that health gains from improvements of the residential neighbourhood may be greater for some people. Copyright © 2015 Elsevier Ltd. All rights reserved.

Hierarchical Distributed-Lag Models: Exploring Varying Geographic Scale and Magnitude in Associations Between the Built Environment and Health.

PubMed

Baek, Jonggyu; Sanchez-Vaznaugh, Emma V; Sánchez, Brisa N

2016-03-15

It is well known that associations between features of the built environment and health depend on the geographic scale used to construct environmental attributes. In the built environment literature, it has long been argued that geographic scales may vary across study locations. However, this hypothesized variation has not been systematically examined due to a lack of available statistical methods. We propose a hierarchical distributed-lag model (HDLM) for estimating the underlying overall shape of food environment-health associations as a function of distance from locations of interest. This method enables indirect assessment of relevant geographic scales and captures area-level heterogeneity in the magnitudes of associations, along with relevant distances within areas. The proposed model was used to systematically examine area-level variation in the association between availability of convenience stores around schools and children's weights. For this case study, body mass index (weight kg)/height (m)2) z scores (BMIz) for 7th grade children collected via California's 2001-2009 FitnessGram testing program were linked to a commercial database that contained locations of food outlets statewide. Findings suggested that convenience store availability may influence BMIz only in some places and at varying distances from schools. Future research should examine localized environmental or policy differences that may explain the heterogeneity in convenience store-BMIz associations. © The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Visual search in scenes involves selective and non-selective pathways

PubMed Central

Wolfe, Jeremy M; Vo, Melissa L-H; Evans, Karla K; Greene, Michelle R

2010-01-01

How do we find objects in scenes? For decades, visual search models have been built on experiments in which observers search for targets, presented among distractor items, isolated and randomly arranged on blank backgrounds. Are these models relevant to search in continuous scenes? This paper argues that the mechanisms that govern artificial, laboratory search tasks do play a role in visual search in scenes. However, scene-based information is used to guide search in ways that had no place in earlier models. Search in scenes may be best explained by a dual-path model: A “selective” path in which candidate objects must be individually selected for recognition and a “non-selective” path in which information can be extracted from global / statistical information. PMID:21227734

PHAST: Protein-like heteropolymer analysis by statistical thermodynamics

NASA Astrophysics Data System (ADS)

Frigori, Rafael B.

2017-06-01

PHAST is a software package written in standard Fortran, with MPI and CUDA extensions, able to efficiently perform parallel multicanonical Monte Carlo simulations of single or multiple heteropolymeric chains, as coarse-grained models for proteins. The outcome data can be straightforwardly analyzed within its microcanonical Statistical Thermodynamics module, which allows for computing the entropy, caloric curve, specific heat and free energies. As a case study, we investigate the aggregation of heteropolymers bioinspired on Aβ25-33 fragments and their cross-seeding with IAPP20-29 isoforms. Excellent parallel scaling is observed, even under numerically difficult first-order like phase transitions, which are properly described by the built-in fully reconfigurable force fields. Still, the package is free and open source, this shall motivate users to readily adapt it to specific purposes.

Iterative model building, structure refinement and density modification with the PHENIX AutoBuild wizard.

PubMed

Terwilliger, Thomas C; Grosse-Kunstleve, Ralf W; Afonine, Pavel V; Moriarty, Nigel W; Zwart, Peter H; Hung, Li Wei; Read, Randy J; Adams, Paul D

2008-01-01

The PHENIX AutoBuild wizard is a highly automated tool for iterative model building, structure refinement and density modification using RESOLVE model building, RESOLVE statistical density modification and phenix.refine structure refinement. Recent advances in the AutoBuild wizard and phenix.refine include automated detection and application of NCS from models as they are built, extensive model-completion algorithms and automated solvent-molecule picking. Model-completion algorithms in the AutoBuild wizard include loop building, crossovers between chains in different models of a structure and side-chain optimization. The AutoBuild wizard has been applied to a set of 48 structures at resolutions ranging from 1.1 to 3.2 A, resulting in a mean R factor of 0.24 and a mean free R factor of 0.29. The R factor of the final model is dependent on the quality of the starting electron density and is relatively independent of resolution.

Use of the Monte Carlo Method for OECD Principles-Guided QSAR Modeling of SIRT1 Inhibitors.

PubMed

Kumar, Ashwani; Chauhan, Shilpi

2017-01-01

SIRT1 inhibitors offer therapeutic potential for the treatment of a number of diseases including cancer and human immunodeficiency virus infection. A diverse series of 45 compounds with reported SIRT1 inhibitory activity has been employed for the development of quantitative structure-activity relationship (QSAR) models using the Monte Carlo optimization method. This method makes use of simplified molecular input line entry system notation of the molecular structure. The QSAR models were built up according to OECD principles. Three subsets of three splits were examined and validated by respective external sets. All the three described models have good statistical quality. The best model has the following statistical characteristics: R 2  = 0.8350, Q 2 test  = 0.7491 for the test set and R 2  = 0.9655, Q 2 ext  = 0.9261 for the validation set. In the mechanistic interpretation, structural attributes responsible for the endpoint increase and decrease are defined. Further, the design of some prospective SIRT1 inhibitors is also presented on the basis of these structural attributes. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A statistical model for predicting the inter-annual variability of birch pollen abundance in Northern and North-Eastern Europe.

PubMed

Ritenberga, Olga; Sofiev, Mikhail; Siljamo, Pilvi; Saarto, Annika; Dahl, Aslog; Ekebom, Agneta; Sauliene, Ingrida; Shalaboda, Valentina; Severova, Elena; Hoebeke, Lucie; Ramfjord, Hallvard

2018-02-15

The paper suggests a methodology for predicting next-year seasonal pollen index (SPI, a sum of daily-mean pollen concentrations) over large regions and demonstrates its performance for birch in Northern and North-Eastern Europe. A statistical model is constructed using meteorological, geophysical and biological characteristics of the previous year). A cluster analysis of multi-annual data of European Aeroallergen Network (EAN) revealed several large regions in Europe, where the observed SPI exhibits similar patterns of the multi-annual variability. We built the model for the northern cluster of stations, which covers Finland, Sweden, Baltic States, part of Belarus, and, probably, Russia and Norway, where the lack of data did not allow for conclusive analysis. The constructed model was capable of predicting the SPI with correlation coefficient reaching up to 0.9 for some stations, odds ratio is infinitely high for 50% of sites inside the region and the fraction of prediction falling within factor of 2 from observations, stays within 40-70%. In particular, model successfully reproduced both the bi-annual cycle of the SPI and years when this cycle breaks down. Copyright © 2017 Elsevier B.V. All rights reserved.

Predicting breeding habitat for amphibians: a spatiotemporal analysis across Yellowstone National Park

USGS Publications Warehouse

Bartelt, Paul E.; Gallant, Alisa L.; Klaver, Robert W.; Wright, Christopher K.; Patla, Debra A.; Peterson, Charles R.

2011-01-01

The ability to predict amphibian breeding across landscapes is important for informing land management decisions and helping biologists better understand and remediate factors contributing to declines in amphibian populations. We built geospatial models of likely breeding habitats for each of four amphibian species that breed in Yellowstone National Park (YNP). We used field data collected in 2000-2002 from 497 sites among 16 basins and predictor variables from geospatial models produced from remotely sensed data (e.g., digital elevation model, complex topographic index, landform data, wetland probabililty, and vegetative cover). Except for 31 sites in one basin that were surveyed in both 2000 and 2002, all sites were surveyed once. We used polytomous regression to build statistical models for each species of amphibian from 1) field survey site data only, 2) field data combined with data from geospatial models, and 3) data from geospatial models only. Based on measures of receiver operating characteristic (ROC) scores, models of the second type best explained likely breeding habitat because they contained the most information (ROC values ranged from 0.70 - 0.88). However, models of the third type could be applied to the entire YNP landscape and produced maps that could be verified with reserve field data. Accuracy rates for models built for single years were highly variable, ranging from 0.30 to 0.78. Accuracy rates for models built with data combined from multiple years were higher and less variable, ranging from 0.60 to 0.80. Combining results from the geospatial multiyear models yielded maps of "core" breeding areas (areas with high probability values for all three years) surrounded by areas that scored high for only one or two years, providing an estimate of variability among years. Such information can highlight landscape options for amphibian conservation. For example, our models identify alternative for areas that could be protected for each species, including 6828-10 764 ha for tiger salamanders; 971-3017 ha for western toads; 4732-16 696 ha for boreal chorus frogs; 4940-19 690 hectares for Columbia spotted frogs.

Predicting breeding habitat for amphibians: A spatiotemporal analysis across Yellowstone National Park

USGS Publications Warehouse

Bartelt, Paul E.; Gallant, Alisa L.; Klaver, Robert W.; Wright, C.K.; Patla, Debra A.; Peterson, Charles R.

2011-01-01

The ability to predict amphibian breeding across landscapes is important for informing land management decisions and helping biologists better understand and remediate factors contributing to declines in amphibian populations. We built geospatial models of likely breeding habitats for each of four amphibian species that breed in Yellowstone National Park (YNP). We used field data collected in 2000-2002 from 497 sites among 16 basins and predictor variables from geospatial models produced from remotely sensed data (e.g., digital elevation model, complex topographic index, landform data, wetland probability, and vegetative cover). Except for 31 sites in one basin that were surveyed in both 2000 and 2002, all sites were surveyed once. We used polytomous regression to build statistical models for each species of amphibian from (1) field survey site data only, (2) field data combined with data from geospatial models, and (3) data from geospatial models only. Based on measures of receiver operating characteristic (ROC) scores, models of the second type best explained likely breeding habitat because they contained the most information (ROC values ranged from 0.70 to 0.88). However, models of the third type could be applied to the entire YNP landscape and produced maps that could be verified with reserve field data. Accuracy rates for models built for single years were highly variable, ranging from 0.30 to 0.78. Accuracy rates for models built with data combined from multiple years were higher and less variable, ranging from 0.60 to 0.80. Combining results from the geospatial multiyear models yielded maps of "core" breeding areas (areas with high probability values for all three years) surrounded by areas that scored high for only one or two years, providing an estimate of variability among years. Such information can highlight landscape options for amphibian conservation. For example, our models identify alternative areas that could be protected for each species, including 6828-10 764 ha for tiger salamanders, 971-3017 ha for western toads, 4732-16 696 ha for boreal chorus frogs, and 4940-19 690 ha for Columbia spotted frogs. ?? 2011 by the Ecological Society of America.

Predicting breeding habitat for amphibians: a spatiotemporal analysis across Yellowstone National Park.

PubMed

Bartelt, Paul E; Gallant, Alisa L; Klaver, Robert W; Wright, Chris K; Patla, Debra A; Peterson, Charles R

2011-10-01

The ability to predict amphibian breeding across landscapes is important for informing land management decisions and helping biologists better understand and remediate factors contributing to declines in amphibian populations. We built geospatial models of likely breeding habitats for each of four amphibian species that breed in Yellowstone National Park (YNP). We used field data collected in 2000-2002 from 497 sites among 16 basins and predictor variables from geospatial models produced from remotely sensed data (e.g., digital elevation model, complex topographic index, landform data, wetland probability, and vegetative cover). Except for 31 sites in one basin that were surveyed in both 2000 and 2002, all sites were surveyed once. We used polytomous regression to build statistical models for each species of amphibian from (1) field survey site data only, (2) field data combined with data from geospatial models, and (3) data from geospatial models only. Based on measures of receiver operating characteristic (ROC) scores, models of the second type best explained likely breeding habitat because they contained the most information (ROC values ranged from 0.70 to 0.88). However, models of the third type could be applied to the entire YNP landscape and produced maps that could be verified with reserve field data. Accuracy rates for models built for single years were highly variable, ranging from 0.30 to 0.78. Accuracy rates for models built with data combined from multiple years were higher and less variable, ranging from 0.60 to 0.80. Combining results from the geospatial multiyear models yielded maps of "core" breeding areas (areas with high probability values for all three years) surrounded by areas that scored high for only one or two years, providing an estimate of variability among years. Such information can highlight landscape options for amphibian conservation. For example, our models identify alternative areas that could be protected for each species, including 6828-10 764 ha for tiger salamanders, 971-3017 ha for western toads, 4732-16 696 ha for boreal chorus frogs, and 4940-19 690 ha for Columbia spotted frogs.

The Child as Econometrician: A Rational Model of Preference Understanding in Children

PubMed Central

Lucas, Christopher G.; Griffiths, Thomas L.; Xu, Fei; Fawcett, Christine; Gopnik, Alison; Kushnir, Tamar; Markson, Lori; Hu, Jane

2014-01-01

Recent work has shown that young children can learn about preferences by observing the choices and emotional reactions of other people, but there is no unified account of how this learning occurs. We show that a rational model, built on ideas from economics and computer science, explains the behavior of children in several experiments, and offers new predictions as well. First, we demonstrate that when children use statistical information to learn about preferences, their inferences match the predictions of a simple econometric model. Next, we show that this same model can explain children's ability to learn that other people have preferences similar to or different from their own and use that knowledge to reason about the desirability of hidden objects. Finally, we use the model to explain a developmental shift in preference understanding. PMID:24667309

The child as econometrician: a rational model of preference understanding in children.

PubMed

Lucas, Christopher G; Griffiths, Thomas L; Xu, Fei; Fawcett, Christine; Gopnik, Alison; Kushnir, Tamar; Markson, Lori; Hu, Jane

2014-01-01

Recent work has shown that young children can learn about preferences by observing the choices and emotional reactions of other people, but there is no unified account of how this learning occurs. We show that a rational model, built on ideas from economics and computer science, explains the behavior of children in several experiments, and offers new predictions as well. First, we demonstrate that when children use statistical information to learn about preferences, their inferences match the predictions of a simple econometric model. Next, we show that this same model can explain children's ability to learn that other people have preferences similar to or different from their own and use that knowledge to reason about the desirability of hidden objects. Finally, we use the model to explain a developmental shift in preference understanding.

Monitoring the expansion of built-up areas in Seberang Perai region, Penang State, Malaysia

NASA Astrophysics Data System (ADS)

Samat, N.

2014-02-01

Rapid urbanization has caused land use transformation and encroachment of built environment into arable agriculture land. Uncontrolled expansion could bring negative impacts to society, space and the environment. Therefore, information on expansion and future spatial pattern of built-up areas would be useful for planners and decision makers in formulating policies towards managing and planning for sustainable urban development. This study demonstrates the usage of Geographic Information System in monitoring the expansion of built-up area in Seberang Perai region, Penang State, Malaysia. Built-up area has increased by approximately 20% between 1990 and 2001 and further increased by 12% between 2001 and 2007. New development is expected to continue encroach into existing open space and agriculture area since those are the only available land in this study area. The information on statistics of the expansion of built-up area and future spatial pattern of urban expansion were useful in planning and managing urban spatial growth.

Prototyping a Distributed Information Retrieval System That Uses Statistical Ranking.

ERIC Educational Resources Information Center

Harman, Donna; And Others

1991-01-01

Built using a distributed architecture, this prototype distributed information retrieval system uses statistical ranking techniques to provide better service to the end user. Distributed architecture was shown to be a feasible alternative to centralized or CD-ROM information retrieval, and user testing of the ranking methodology showed both…

An Experimental Approach to Teaching and Learning Elementary Statistical Mechanics

ERIC Educational Resources Information Center

Ellis, Frank B.; Ellis, David C.

2008-01-01

Introductory statistical mechanics is studied for a simple two-state system using an inexpensive and easily built apparatus. A large variety of demonstrations, suitable for students in high school and introductory university chemistry courses, are possible. This article details demonstrations for exothermic and endothermic reactions, the dynamic…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Los Alamos National Laboratory, Mailstop M888, Los Alamos, NM 87545, USA; Lawrence Berkeley National Laboratory, One Cyclotron Road, Building 64R0121, Berkeley, CA 94720, USA; Department of Haematology, University of Cambridge, Cambridge CB2 0XY, England

The PHENIX AutoBuild Wizard is a highly automated tool for iterative model-building, structure refinement and density modification using RESOLVE or TEXTAL model-building, RESOLVE statistical density modification, and phenix.refine structure refinement. Recent advances in the AutoBuild Wizard and phenix.refine include automated detection and application of NCS from models as they are built, extensive model completion algorithms, and automated solvent molecule picking. Model completion algorithms in the AutoBuild Wizard include loop-building, crossovers between chains in different models of a structure, and side-chain optimization. The AutoBuild Wizard has been applied to a set of 48 structures at resolutions ranging from 1.1 {angstrom} tomore » 3.2 {angstrom}, resulting in a mean R-factor of 0.24 and a mean free R factor of 0.29. The R-factor of the final model is dependent on the quality of the starting electron density, and relatively independent of resolution.« less

The Center for Computational Biology: resources, achievements, and challenges

PubMed Central

Dinov, Ivo D; Thompson, Paul M; Woods, Roger P; Van Horn, John D; Shattuck, David W; Parker, D Stott

2011-01-01

The Center for Computational Biology (CCB) is a multidisciplinary program where biomedical scientists, engineers, and clinicians work jointly to combine modern mathematical and computational techniques, to perform phenotypic and genotypic studies of biological structure, function, and physiology in health and disease. CCB has developed a computational framework built around the Manifold Atlas, an integrated biomedical computing environment that enables statistical inference on biological manifolds. These manifolds model biological structures, features, shapes, and flows, and support sophisticated morphometric and statistical analyses. The Manifold Atlas includes tools, workflows, and services for multimodal population-based modeling and analysis of biological manifolds. The broad spectrum of biomedical topics explored by CCB investigators include the study of normal and pathological brain development, maturation and aging, discovery of associations between neuroimaging and genetic biomarkers, and the modeling, analysis, and visualization of biological shape, form, and size. CCB supports a wide range of short-term and long-term collaborations with outside investigators, which drive the center's computational developments and focus the validation and dissemination of CCB resources to new areas and scientific domains. PMID:22081221

The Center for Computational Biology: resources, achievements, and challenges.

PubMed

Toga, Arthur W; Dinov, Ivo D; Thompson, Paul M; Woods, Roger P; Van Horn, John D; Shattuck, David W; Parker, D Stott

2012-01-01

The Center for Computational Biology (CCB) is a multidisciplinary program where biomedical scientists, engineers, and clinicians work jointly to combine modern mathematical and computational techniques, to perform phenotypic and genotypic studies of biological structure, function, and physiology in health and disease. CCB has developed a computational framework built around the Manifold Atlas, an integrated biomedical computing environment that enables statistical inference on biological manifolds. These manifolds model biological structures, features, shapes, and flows, and support sophisticated morphometric and statistical analyses. The Manifold Atlas includes tools, workflows, and services for multimodal population-based modeling and analysis of biological manifolds. The broad spectrum of biomedical topics explored by CCB investigators include the study of normal and pathological brain development, maturation and aging, discovery of associations between neuroimaging and genetic biomarkers, and the modeling, analysis, and visualization of biological shape, form, and size. CCB supports a wide range of short-term and long-term collaborations with outside investigators, which drive the center's computational developments and focus the validation and dissemination of CCB resources to new areas and scientific domains.

ASCS online fault detection and isolation based on an improved MPCA

NASA Astrophysics Data System (ADS)

Peng, Jianxin; Liu, Haiou; Hu, Yuhui; Xi, Junqiang; Chen, Huiyan

2014-09-01

Multi-way principal component analysis (MPCA) has received considerable attention and been widely used in process monitoring. A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces. However, low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model. This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information. The MPCA model and the knowledge base are built based on the new subspace. Then, fault detection and isolation with the squared prediction error (SPE) statistic and the Hotelling ( T 2) statistic are also realized in process monitoring. When a fault occurs, fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables. For fault isolation of subspace based on the T 2 statistic, the relationship between the statistic indicator and state variables is constructed, and the constraint conditions are presented to check the validity of fault isolation. Then, to improve the robustness of fault isolation to unexpected disturbances, the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation. Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system (ASCS) to prove the correctness and effectiveness of the algorithm. The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model, and sets the relationship between the state variables and fault detection indicators for fault isolation.

Quantum theory of multiscale coarse-graining.

PubMed

Han, Yining; Jin, Jaehyeok; Wagner, Jacob W; Voth, Gregory A

2018-03-14

Coarse-grained (CG) models serve as a powerful tool to simulate molecular systems at much longer temporal and spatial scales. Previously, CG models and methods have been built upon classical statistical mechanics. The present paper develops a theory and numerical methodology for coarse-graining in quantum statistical mechanics, by generalizing the multiscale coarse-graining (MS-CG) method to quantum Boltzmann statistics. A rigorous derivation of the sufficient thermodynamic consistency condition is first presented via imaginary time Feynman path integrals. It identifies the optimal choice of CG action functional and effective quantum CG (qCG) force field to generate a quantum MS-CG (qMS-CG) description of the equilibrium system that is consistent with the quantum fine-grained model projected onto the CG variables. A variational principle then provides a class of algorithms for optimally approximating the qMS-CG force fields. Specifically, a variational method based on force matching, which was also adopted in the classical MS-CG theory, is generalized to quantum Boltzmann statistics. The qMS-CG numerical algorithms and practical issues in implementing this variational minimization procedure are also discussed. Then, two numerical examples are presented to demonstrate the method. Finally, as an alternative strategy, a quasi-classical approximation for the thermal density matrix expressed in the CG variables is derived. This approach provides an interesting physical picture for coarse-graining in quantum Boltzmann statistical mechanics in which the consistency with the quantum particle delocalization is obviously manifest, and it opens up an avenue for using path integral centroid-based effective classical force fields in a coarse-graining methodology.

Nuclear reactors built, being built, or planned 1996

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1997-08-01

This publication contains unclassified information about facilities, built, being built, or planned in the United States for domestic use or export as of December 31, 1996. The Office of Scientific and Technical Information, U.S. Department of Energy, gathers this information annually from Washington headquarters, and field offices of DOE; from the U.S. Nuclear Regulatory Commission (NRC); from the U. S. reactor manufacturers who are the principal nuclear contractors for foreign reactor locations; from U.S. and foreign embassies; and from foreign governmental nuclear departments. The book consists of three divisions, as follows: (1) a commercial reactor locator map and tables ofmore » the characteristic and statistical data that follow; a table of abbreviations; (2) tables of data for reactors operating, being built, or planned; and (3) tables of data for reactors that have been shut down permanently or dismantled.« less

Does rational selection of training and test sets improve the outcome of QSAR modeling?

PubMed

Martin, Todd M; Harten, Paul; Young, Douglas M; Muratov, Eugene N; Golbraikh, Alexander; Zhu, Hao; Tropsha, Alexander

2012-10-22

Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external data set, the best way to validate the predictive ability of a model is to perform its statistical external validation. In statistical external validation, the overall data set is divided into training and test sets. Commonly, this splitting is performed using random division. Rational splitting methods can divide data sets into training and test sets in an intelligent fashion. The purpose of this study was to determine whether rational division methods lead to more predictive models compared to random division. A special data splitting procedure was used to facilitate the comparison between random and rational division methods. For each toxicity end point, the overall data set was divided into a modeling set (80% of the overall set) and an external evaluation set (20% of the overall set) using random division. The modeling set was then subdivided into a training set (80% of the modeling set) and a test set (20% of the modeling set) using rational division methods and by using random division. The Kennard-Stone, minimal test set dissimilarity, and sphere exclusion algorithms were used as the rational division methods. The hierarchical clustering, random forest, and k-nearest neighbor (kNN) methods were used to develop QSAR models based on the training sets. For kNN QSAR, multiple training and test sets were generated, and multiple QSAR models were built. The results of this study indicate that models based on rational division methods generate better statistical results for the test sets than models based on random division, but the predictive power of both types of models are comparable.

Estimating Active Transportation Behaviors to Support Health Impact Assessment in the United States

PubMed Central

Mansfield, Theodore J.; Gibson, Jacqueline MacDonald

2016-01-01

Health impact assessment (HIA) has been promoted as a means to encourage transportation and city planners to incorporate health considerations into their decision-making. Ideally, HIAs would include quantitative estimates of the population health effects of alternative planning scenarios, such as scenarios with and without infrastructure to support walking and cycling. However, the lack of baseline estimates of time spent walking or biking for transportation (together known as “active transportation”), which are critically related to health, often prevents planners from developing such quantitative estimates. To address this gap, we use data from the 2009 US National Household Travel Survey to develop a statistical model that estimates baseline time spent walking and biking as a function of the type of transportation used to commute to work along with demographic and built environment variables. We validate the model using survey data from the Raleigh–Durham–Chapel Hill, NC, USA, metropolitan area. We illustrate how the validated model could be used to support transportation-related HIAs by estimating the potential health benefits of built environment modifications that support walking and cycling. Our statistical model estimates that on average, individuals who commute on foot spend an additional 19.8 (95% CI 16.9–23.2) minutes per day walking compared to automobile commuters. Public transit riders walk an additional 5.0 (95% CI 3.5–6.4) minutes per day compared to automobile commuters. Bicycle commuters cycle for an additional 28.0 (95% CI 17.5–38.1) minutes per day compared to automobile commuters. The statistical model was able to predict observed transportation physical activity in the Raleigh–Durham–Chapel Hill region to within 0.5 MET-hours per day (equivalent to about 9 min of daily walking time) for 83% of observations. Across the Raleigh–Durham–Chapel Hill region, an estimated 38 (95% CI 15–59) premature deaths potentially could be avoided if the entire population walked 37.4 min per week for transportation (the amount of transportation walking observed in previous US studies of walkable neighborhoods). The approach developed here is useful both for estimating baseline behaviors in transportation HIAs and for comparing the magnitude of risks associated with physical inactivity to other competing health risks in urban areas. PMID:27200327

Estimating Active Transportation Behaviors to Support Health Impact Assessment in the United States.

PubMed

Mansfield, Theodore J; Gibson, Jacqueline MacDonald

2016-01-01

Health impact assessment (HIA) has been promoted as a means to encourage transportation and city planners to incorporate health considerations into their decision-making. Ideally, HIAs would include quantitative estimates of the population health effects of alternative planning scenarios, such as scenarios with and without infrastructure to support walking and cycling. However, the lack of baseline estimates of time spent walking or biking for transportation (together known as "active transportation"), which are critically related to health, often prevents planners from developing such quantitative estimates. To address this gap, we use data from the 2009 US National Household Travel Survey to develop a statistical model that estimates baseline time spent walking and biking as a function of the type of transportation used to commute to work along with demographic and built environment variables. We validate the model using survey data from the Raleigh-Durham-Chapel Hill, NC, USA, metropolitan area. We illustrate how the validated model could be used to support transportation-related HIAs by estimating the potential health benefits of built environment modifications that support walking and cycling. Our statistical model estimates that on average, individuals who commute on foot spend an additional 19.8 (95% CI 16.9-23.2) minutes per day walking compared to automobile commuters. Public transit riders walk an additional 5.0 (95% CI 3.5-6.4) minutes per day compared to automobile commuters. Bicycle commuters cycle for an additional 28.0 (95% CI 17.5-38.1) minutes per day compared to automobile commuters. The statistical model was able to predict observed transportation physical activity in the Raleigh-Durham-Chapel Hill region to within 0.5 MET-hours per day (equivalent to about 9 min of daily walking time) for 83% of observations. Across the Raleigh-Durham-Chapel Hill region, an estimated 38 (95% CI 15-59) premature deaths potentially could be avoided if the entire population walked 37.4 min per week for transportation (the amount of transportation walking observed in previous US studies of walkable neighborhoods). The approach developed here is useful both for estimating baseline behaviors in transportation HIAs and for comparing the magnitude of risks associated with physical inactivity to other competing health risks in urban areas.

Indiana chronic disease management program risk stratification analysis.

PubMed

Li, Jingjin; Holmes, Ann M; Rosenman, Marc B; Katz, Barry P; Downs, Stephen M; Murray, Michael D; Ackermann, Ronald T; Inui, Thomas S

2005-10-01

The objective of this study was to compare the ability of risk stratification models derived from administrative data to classify groups of patients for enrollment in a tailored chronic disease management program. This study included 19,548 Medicaid patients with chronic heart failure or diabetes in the Indiana Medicaid data warehouse during 2001 and 2002. To predict costs (total claims paid) in FY 2002, we considered candidate predictor variables available in FY 2001, including patient characteristics, the number and type of prescription medications, laboratory tests, pharmacy charges, and utilization of primary, specialty, inpatient, emergency department, nursing home, and home health care. We built prospective models to identify patients with different levels of expenditure. Model fit was assessed using R statistics, whereas discrimination was assessed using the weighted kappa statistic, predictive ratios, and the area under the receiver operating characteristic curve. We found a simple least-squares regression model in which logged total charges in FY 2002 were regressed on the log of total charges in FY 2001, the number of prescriptions filled in FY 2001, and the FY 2001 eligibility category, performed as well as more complex models. This simple 3-parameter model had an R of 0.30 and, in terms in classification efficiency, had a sensitivity of 0.57, a specificity of 0.90, an area under the receiver operator curve of 0.80, and a weighted kappa statistic of 0.51. This simple model based on readily available administrative data stratified Medicaid members according to predicted future utilization as well as more complicated models.

Sharpening method of satellite thermal image based on the geographical statistical model

NASA Astrophysics Data System (ADS)

Qi, Pengcheng; Hu, Shixiong; Zhang, Haijun; Guo, Guangmeng

2016-04-01

To improve the effectiveness of thermal sharpening in mountainous regions, paying more attention to the laws of land surface energy balance, a thermal sharpening method based on the geographical statistical model (GSM) is proposed. Explanatory variables were selected from the processes of land surface energy budget and thermal infrared electromagnetic radiation transmission, then high spatial resolution (57 m) raster layers were generated for these variables through spatially simulating or using other raster data as proxies. Based on this, the local adaptation statistical relationship between brightness temperature (BT) and the explanatory variables, i.e., the GSM, was built at 1026-m resolution using the method of multivariate adaptive regression splines. Finally, the GSM was applied to the high-resolution (57-m) explanatory variables; thus, the high-resolution (57-m) BT image was obtained. This method produced a sharpening result with low error and good visual effect. The method can avoid the blind choice of explanatory variables and remove the dependence on synchronous imagery at visible and near-infrared bands. The influences of the explanatory variable combination, sampling method, and the residual error correction on sharpening results were analyzed deliberately, and their influence mechanisms are reported herein.

Coupling of Markov chains and cellular automata spatial models to predict land cover changes (case study: upper Ci Leungsi catchment area)

NASA Astrophysics Data System (ADS)

Marko, K.; Zulkarnain, F.; Kusratmoko, E.

2016-11-01

Land cover changes particular in urban catchment area has been rapidly occur. Land cover changes occur as a result of increasing demand for built-up area. Various kinds of environmental and hydrological problems e.g. floods and urban heat island can happen if the changes are uncontrolled. This study aims to predict land cover changes using coupling of Markov chains and cellular automata. One of the most rapid land cover changes is occurs at upper Ci Leungsi catchment area that located near Bekasi City and Jakarta Metropolitan Area. Markov chains has a good ability to predict the probability of change statistically while cellular automata believed as a powerful method in reading the spatial patterns of change. Temporal land cover data was obtained by remote sensing satellite imageries. In addition, this study also used multi-criteria analysis to determine which driving factor that could stimulate the changes such as proximity, elevation, and slope. Coupling of these two methods could give better prediction model rather than just using it separately. The prediction model was validated using existing 2015 land cover data and shown a satisfactory kappa coefficient. The most significant increasing land cover is built-up area from 24% to 53%.

40 CFR 86.401-97 - General applicability.

Code of Federal Regulations, 2010 CFR

2010-07-01

... applies to 1978 and later model year, new, gasoline-fueled motorcycles built after 31 December, 1977, and to 1990 and later model year, new, methanol-fueled motorcycles built after 31 December, 1989 and to 1997 and later model year, new, natural gas-fueled and liquefied petroleum gas-fueled motorcycles built...

40 CFR 86.401-2006 - General applicability.

Code of Federal Regulations, 2010 CFR

2010-07-01

... applies to 1978 and later model year, new, gasoline-fueled motorcycles built after December 31, 1977, and to 1990 and later model year, new methanol-fueled motorcycles built after December 31, 1989 and to 1997 and later model year, new natural gas-fueled and liquefied petroleum gas-fueled motorcycles built...

Built-In-Test Equipment Requirements Workshop. Workshop Presentation

DTIC Science & Technology

1981-08-01

quantitatively evaluated in test. (2) It is necessary to develop the statistical methods that should be used for predicting and confirming of diagnostic...of different performance levels of BIT peacetime and wartime applications, and the corresponding manpower and other support requirements should be...reports. The scope of the workshop involves the areas of require- ments for built-in-test and diagnostics, and the methods of testing to ensure that the

Statistical detection of systematic election irregularities

PubMed Central

Klimek, Peter; Yegorov, Yuri; Hanel, Rudolf; Thurner, Stefan

2012-01-01

Democratic societies are built around the principle of free and fair elections, and that each citizen’s vote should count equally. National elections can be regarded as large-scale social experiments, where people are grouped into usually large numbers of electoral districts and vote according to their preferences. The large number of samples implies statistical consequences for the polling results, which can be used to identify election irregularities. Using a suitable data representation, we find that vote distributions of elections with alleged fraud show a kurtosis substantially exceeding the kurtosis of normal elections, depending on the level of data aggregation. As an example, we show that reported irregularities in recent Russian elections are, indeed, well-explained by systematic ballot stuffing. We develop a parametric model quantifying the extent to which fraudulent mechanisms are present. We formulate a parametric test detecting these statistical properties in election results. Remarkably, this technique produces robust outcomes with respect to the resolution of the data and therefore, allows for cross-country comparisons. PMID:23010929

Statistical detection of systematic election irregularities.

PubMed

Klimek, Peter; Yegorov, Yuri; Hanel, Rudolf; Thurner, Stefan

2012-10-09

Democratic societies are built around the principle of free and fair elections, and that each citizen's vote should count equally. National elections can be regarded as large-scale social experiments, where people are grouped into usually large numbers of electoral districts and vote according to their preferences. The large number of samples implies statistical consequences for the polling results, which can be used to identify election irregularities. Using a suitable data representation, we find that vote distributions of elections with alleged fraud show a kurtosis substantially exceeding the kurtosis of normal elections, depending on the level of data aggregation. As an example, we show that reported irregularities in recent Russian elections are, indeed, well-explained by systematic ballot stuffing. We develop a parametric model quantifying the extent to which fraudulent mechanisms are present. We formulate a parametric test detecting these statistical properties in election results. Remarkably, this technique produces robust outcomes with respect to the resolution of the data and therefore, allows for cross-country comparisons.

Bootstrap study of genome-enabled prediction reliabilities using haplotype blocks across Nordic Red cattle breeds.

PubMed

Cuyabano, B C D; Su, G; Rosa, G J M; Lund, M S; Gianola, D

2015-10-01

This study compared the accuracy of genome-enabled prediction models using individual single nucleotide polymorphisms (SNP) or haplotype blocks as covariates when using either a single breed or a combined population of Nordic Red cattle. The main objective was to compare predictions of breeding values of complex traits using a combined training population with haplotype blocks, with predictions using a single breed as training population and individual SNP as predictors. To compare the prediction reliabilities, bootstrap samples were taken from the test data set. With the bootstrapped samples of prediction reliabilities, we built and graphed confidence ellipses to allow comparisons. Finally, measures of statistical distances were used to calculate the gain in predictive ability. Our analyses are innovative in the context of assessment of predictive models, allowing a better understanding of prediction reliabilities and providing a statistical basis to effectively calibrate whether one prediction scenario is indeed more accurate than another. An ANOVA indicated that use of haplotype blocks produced significant gains mainly when Bayesian mixture models were used but not when Bayesian BLUP was fitted to the data. Furthermore, when haplotype blocks were used to train prediction models in a combined Nordic Red cattle population, we obtained up to a statistically significant 5.5% average gain in prediction accuracy, over predictions using individual SNP and training the model with a single breed. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

The geostatistical approach for structural and stratigraphic framework analysis of offshore NW Bonaparte Basin, Australia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wahid, Ali, E-mail: ali.wahid@live.com; Salim, Ahmed Mohamed Ahmed, E-mail: mohamed.salim@petronas.com.my; Yusoff, Wan Ismail Wan, E-mail: wanismail-wanyusoff@petronas.com.my

2016-02-01

Geostatistics or statistical approach is based on the studies of temporal and spatial trend, which depend upon spatial relationships to model known information of variable(s) at unsampled locations. The statistical technique known as kriging was used for petrophycial and facies analysis, which help to assume spatial relationship to model the geological continuity between the known data and the unknown to produce a single best guess of the unknown. Kriging is also known as optimal interpolation technique, which facilitate to generate best linear unbiased estimation of each horizon. The idea is to construct a numerical model of the lithofacies and rockmore » properties that honor available data and further integrate with interpreting seismic sections, techtonostratigraphy chart with sea level curve (short term) and regional tectonics of the study area to find the structural and stratigraphic growth history of the NW Bonaparte Basin. By using kriging technique the models were built which help to estimate different parameters like horizons, facies, and porosities in the study area. The variograms were used to determine for identification of spatial relationship between data which help to find the depositional history of the North West (NW) Bonaparte Basin.« less

Building and verifying a severity prediction model of acute pancreatitis (AP) based on BISAP, MEWS and routine test indexes.

PubMed

Ye, Jiang-Feng; Zhao, Yu-Xin; Ju, Jian; Wang, Wei

2017-10-01

To discuss the value of the Bedside Index for Severity in Acute Pancreatitis (BISAP), Modified Early Warning Score (MEWS), serum Ca2+, similarly hereinafter, and red cell distribution width (RDW) for predicting the severity grade of acute pancreatitis and to develop and verify a more accurate scoring system to predict the severity of AP. In 302 patients with AP, we calculated BISAP and MEWS scores and conducted regression analyses on the relationships of BISAP scoring, RDW, MEWS, and serum Ca2+ with the severity of AP using single-factor logistics. The variables with statistical significance in the single-factor logistic regression were used in a multi-factor logistic regression model; forward stepwise regression was used to screen variables and build a multi-factor prediction model. A receiver operating characteristic curve (ROC curve) was constructed, and the significance of multi- and single-factor prediction models in predicting the severity of AP using the area under the ROC curve (AUC) was evaluated. The internal validity of the model was verified through bootstrapping. Among 302 patients with AP, 209 had mild acute pancreatitis (MAP) and 93 had severe acute pancreatitis (SAP). According to single-factor logistic regression analysis, we found that BISAP, MEWS and serum Ca2+ are prediction indexes of the severity of AP (P-value<0.001), whereas RDW is not a prediction index of AP severity (P-value>0.05). The multi-factor logistic regression analysis showed that BISAP and serum Ca2+ are independent prediction indexes of AP severity (P-value<0.001), and MEWS is not an independent prediction index of AP severity (P-value>0.05); BISAP is negatively related to serum Ca2+ (r=-0.330, P-value<0.001). The constructed model is as follows: ln()=7.306+1.151*BISAP-4.516*serum Ca2+. The predictive ability of each model for SAP follows the order of the combined BISAP and serum Ca2+ prediction model>Ca2+>BISAP. There is no statistical significance for the predictive ability of BISAP and serum Ca2+ (P-value>0.05); however, there is remarkable statistical significance for the predictive ability using the newly built prediction model as well as BISAP and serum Ca2+ individually (P-value<0.01). Verification of the internal validity of the models by bootstrapping is favorable. BISAP and serum Ca2+ have high predictive value for the severity of AP. However, the model built by combining BISAP and serum Ca2+ is remarkably superior to those of BISAP and serum Ca2+ individually. Furthermore, this model is simple, practical and appropriate for clinical use. Copyright © 2016. Published by Elsevier Masson SAS.

An Online Course of Business Statistics: The Proportion of Successful Students

ERIC Educational Resources Information Center

Pena-Sanchez, Rolando

2009-01-01

This article describes the students' academic progress in an online course of business statistics through interactive software assignments and diverse educational homework, which helps these students to build their own e-learning through basic competences; i.e. interpreting results and solving problems. Cross-tables were built for the categorical…

Exploring Foundation Concepts in Introductory Statistics Using Dynamic Data Points

ERIC Educational Resources Information Center

Ekol, George

2015-01-01

This paper analyses introductory statistics students' verbal and gestural expressions as they interacted with a dynamic sketch (DS) designed using "Sketchpad" software. The DS involved numeric data points built on the number line whose values changed as the points were dragged along the number line. The study is framed on aggregate…

The Effect of Anchor Test Construction on Scale Drift

ERIC Educational Resources Information Center

Antal, Judit; Proctor, Thomas P.; Melican, Gerald J.

2014-01-01

In common-item equating the anchor block is generally built to represent a miniature form of the total test in terms of content and statistical specifications. The statistical properties frequently reflect equal mean and spread of item difficulty. Sinharay and Holland (2007) suggested that the requirement for equal spread of difficulty may be too…

Using statistical deformable models to reconstruct vocal tract shape from magnetic resonance images.

PubMed

Vasconcelos, M J M; Rua Ventura, S M; Freitas, D R S; Tavares, J M R S

2010-10-01

The mechanisms involved in speech production are complex and have thus been subject to growing attention by the scientific community. It has been demonstrated that magnetic resonance imaging (MRI) is a powerful means in the understanding of the morphology of the vocal tract. Over the last few years, statistical deformable models have been successfully used to identify and characterize bones and organs in medical images and point distribution models (PDMs) have gained particular relevance. In this work, the suitability of these models has been studied to characterize and further reconstruct the shape of the vocal tract in the articulation of Portuguese European (EP) speech sounds, one of the most spoken languages worldwide, with the aid of MR images. Therefore, a PDM has been built from a set of MR images acquired during the artificially sustained articulation of 25 EP speech sounds. Following this, the capacity of this statistical model to characterize the shape deformation of the vocal tract during the production of sounds was analysed. Next, the model was used to reconstruct five EP oral vowels and the EP fricative consonants. As far as a study on speech production is concerned, this study is considered to be the first approach to characterize and reconstruct the vocal tract shape from MR images by using PDMs. In addition, the findings achieved permit one to conclude that this modelling technique compels an enhanced understanding of the dynamic speech events involved in sustained articulations based on MRI, which are of particular interest for speech rehabilitation and simulation.

Incorporating GIS and remote sensing for census population disaggregation

NASA Astrophysics Data System (ADS)

Wu, Shuo-Sheng'derek'

Census data are the primary source of demographic data for a variety of researches and applications. For confidentiality issues and administrative purposes, census data are usually released to the public by aggregated areal units. In the United States, the smallest census unit is census blocks. Due to data aggregation, users of census data may have problems in visualizing population distribution within census blocks and estimating population counts for areas not coinciding with census block boundaries. The main purpose of this study is to develop methodology for estimating sub-block areal populations and assessing the estimation errors. The City of Austin, Texas was used as a case study area. Based on tax parcel boundaries and parcel attributes derived from ancillary GIS and remote sensing data, detailed urban land use classes were first classified using a per-field approach. After that, statistical models by land use classes were built to infer population density from other predictor variables, including four census demographic statistics (the Hispanic percentage, the married percentage, the unemployment rate, and per capita income) and three physical variables derived from remote sensing images and building footprints vector data (a landscape heterogeneity statistics, a building pattern statistics, and a building volume statistics). In addition to statistical models, deterministic models were proposed to directly infer populations from building volumes and three housing statistics, including the average space per housing unit, the housing unit occupancy rate, and the average household size. After population models were derived or proposed, how well the models predict populations for another set of sample blocks was assessed. The results show that deterministic models were more accurate than statistical models. Further, by simulating the base unit for modeling from aggregating blocks, I assessed how well the deterministic models estimate sub-unit-level populations. I also assessed the aggregation effects and the resealing effects on sub-unit estimates. Lastly, from another set of mixed-land-use sample blocks, a mixed-land-use model was derived and compared with a residential-land-use model. The results of per-field land use classification are satisfactory with a Kappa accuracy statistics of 0.747. Model Assessments by land use show that population estimates for multi-family land use areas have higher errors than those for single-family land use areas, and population estimates for mixed land use areas have higher errors than those for residential land use areas. The assessments of sub-unit estimates using a simulation approach indicate that smaller areas show higher estimation errors, estimation errors do not relate to the base unit size, and resealing improves all levels of sub-unit estimates.

Hierarchy and extremes in selections from pools of randomized proteins

PubMed Central

Boyer, Sébastien; Biswas, Dipanwita; Kumar Soshee, Ananda; Scaramozzino, Natale; Nizak, Clément; Rivoire, Olivier

2016-01-01

Variation and selection are the core principles of Darwinian evolution, but quantitatively relating the diversity of a population to its capacity to respond to selection is challenging. Here, we examine this problem at a molecular level in the context of populations of partially randomized proteins selected for binding to well-defined targets. We built several minimal protein libraries, screened them in vitro by phage display, and analyzed their response to selection by high-throughput sequencing. A statistical analysis of the results reveals two main findings. First, libraries with the same sequence diversity but built around different “frameworks” typically have vastly different responses; second, the distribution of responses of the best binders in a library follows a simple scaling law. We show how an elementary probabilistic model based on extreme value theory rationalizes the latter finding. Our results have implications for designing synthetic protein libraries, estimating the density of functional biomolecules in sequence space, characterizing diversity in natural populations, and experimentally investigating evolvability (i.e., the potential for future evolution). PMID:26969726

Hierarchy and extremes in selections from pools of randomized proteins.

PubMed

Boyer, Sébastien; Biswas, Dipanwita; Kumar Soshee, Ananda; Scaramozzino, Natale; Nizak, Clément; Rivoire, Olivier

2016-03-29

Variation and selection are the core principles of Darwinian evolution, but quantitatively relating the diversity of a population to its capacity to respond to selection is challenging. Here, we examine this problem at a molecular level in the context of populations of partially randomized proteins selected for binding to well-defined targets. We built several minimal protein libraries, screened them in vitro by phage display, and analyzed their response to selection by high-throughput sequencing. A statistical analysis of the results reveals two main findings. First, libraries with the same sequence diversity but built around different "frameworks" typically have vastly different responses; second, the distribution of responses of the best binders in a library follows a simple scaling law. We show how an elementary probabilistic model based on extreme value theory rationalizes the latter finding. Our results have implications for designing synthetic protein libraries, estimating the density of functional biomolecules in sequence space, characterizing diversity in natural populations, and experimentally investigating evolvability (i.e., the potential for future evolution).

Spacecraft Internal Acoustic Environment Modeling

NASA Technical Reports Server (NTRS)

Chu, Shao-Sheng R.; Allen Christopher S.

2010-01-01

Acoustic modeling can be used to identify key noise sources, determine/analyze sub-allocated requirements, keep track of the accumulation of minor noise sources, and to predict vehicle noise levels at various stages in vehicle development, first with estimates of noise sources, later with experimental data. This paper describes the implementation of acoustic modeling for design purposes by incrementally increasing model fidelity and validating the accuracy of the model while predicting the noise of sources under various conditions. During FY 07, a simple-geometry Statistical Energy Analysis (SEA) model was developed and validated using a physical mockup and acoustic measurements. A process for modeling the effects of absorptive wall treatments and the resulting reverberation environment were developed. During FY 08, a model with more complex and representative geometry of the Orion Crew Module (CM) interior was built, and noise predictions based on input noise sources were made. A corresponding physical mockup was also built. Measurements were made inside this mockup, and comparisons were made with the model and showed excellent agreement. During FY 09, the fidelity of the mockup and corresponding model were increased incrementally by including a simple ventilation system. The airborne noise contribution of the fans was measured using a sound intensity technique, since the sound power levels were not known beforehand. This is opposed to earlier studies where Reference Sound Sources (RSS) with known sound power level were used. Comparisons of the modeling result with the measurements in the mockup showed excellent results. During FY 10, the fidelity of the mockup and the model were further increased by including an ECLSS (Environmental Control and Life Support System) wall, associated closeout panels, and the gap between ECLSS wall and mockup wall. The effect of sealing the gap and adding sound absorptive treatment to ECLSS wall were also modeled and validated.

Capturing the Interrelationship between Objectively Measured Physical Activity and Sedentary Behaviour in Children in the Context of Diverse Environmental Exposures.

PubMed

Katapally, Tarun R; Muhajarine, Nazeem

2015-09-07

Even though physical activity and sedentary behaviour are two distinct behaviours, their interdependent relationship needs to be studied in the same environment. This study examines the influence of urban design, neighbourhood built and social environment, and household and individual factors on the interdependent relationship between objectively measured physical activity and sedentary behaviour in children in the Canadian city of Saskatoon. Saskatoon's built environment was assessed by two validated observation tools. Neighbourhood socioeconomic variables were derived from 2006 Statistics Canada Census and 2010 G5 Census projections. A questionnaire was administered to 10-14 year old children to collect individual and household data, followed by accelerometry to collect physical activity and sedentary behaviour data. Multilevel logistic regression models were developed to understand the interrelationship between physical activity and sedentary behaviour in the context of diverse environmental exposures. A complex set of factors including denser built environment, positive peer relationships and consistent parental support influenced the interrelationship between physical activity and sedentary behaviour. In developing interventions to facilitate active living, it is not only imperative to delineate pathways through which diverse environmental exposures influence physical activity and sedentary behaviour, but also to account for the interrelationship between physical activity and sedentary behaviour.

Exploiting Data Missingness in Bayesian Network Modeling

NASA Astrophysics Data System (ADS)

Rodrigues de Morais, Sérgio; Aussem, Alex

This paper proposes a framework built on the use of Bayesian networks (BN) for representing statistical dependencies between the existing random variables and additional dummy boolean variables, which represent the presence/absence of the respective random variable value. We show how augmenting the BN with these additional variables helps pinpoint the mechanism through which missing data contributes to the classification task. The missing data mechanism is thus explicitly taken into account to predict the class variable using the data at hand. Extensive experiments on synthetic and real-world incomplete data sets reveals that the missingness information improves classification accuracy.

Evaluation of dynamical models: dissipative synchronization and other techniques.

PubMed

Aguirre, Luis Antonio; Furtado, Edgar Campos; Tôrres, Leonardo A B

2006-12-01

Some recent developments for the validation of nonlinear models built from data are reviewed. Besides giving an overall view of the field, a procedure is proposed and investigated based on the concept of dissipative synchronization between the data and the model, which is very useful in validating models that should reproduce dominant dynamical features, like bifurcations, of the original system. In order to assess the discriminating power of the procedure, four well-known benchmarks have been used: namely, Duffing-Ueda, Duffing-Holmes, and van der Pol oscillators, plus the Hénon map. The procedure, developed for discrete-time systems, is focused on the dynamical properties of the model, rather than on statistical issues. For all the systems investigated, it is shown that the discriminating power of the procedure is similar to that of bifurcation diagrams--which in turn is much greater than, say, that of correlation dimension--but at a much lower computational cost.

Bringing modeling to the masses: A web based system to predict potential species distributions

USGS Publications Warehouse

Graham, Jim; Newman, Greg; Kumar, Sunil; Jarnevich, Catherine S.; Young, Nick; Crall, Alycia W.; Stohlgren, Thomas J.; Evangelista, Paul

2010-01-01

Predicting current and potential species distributions and abundance is critical for managing invasive species, preserving threatened and endangered species, and conserving native species and habitats. Accurate predictive models are needed at local, regional, and national scales to guide field surveys, improve monitoring, and set priorities for conservation and restoration. Modeling capabilities, however, are often limited by access to software and environmental data required for predictions. To address these needs, we built a comprehensive web-based system that: (1) maintains a large database of field data; (2) provides access to field data and a wealth of environmental data; (3) accesses values in rasters representing environmental characteristics; (4) runs statistical spatial models; and (5) creates maps that predict the potential species distribution. The system is available online at www.niiss.org, and provides web-based tools for stakeholders to create potential species distribution models and maps under current and future climate scenarios.

Statistical emulation of landslide-induced tsunamis at the Rockall Bank, NE Atlantic

PubMed Central

Guillas, S.; Georgiopoulou, A.; Dias, F.

2017-01-01

Statistical methods constitute a useful approach to understand and quantify the uncertainty that governs complex tsunami mechanisms. Numerical experiments may often have a high computational cost. This forms a limiting factor for performing uncertainty and sensitivity analyses, where numerous simulations are required. Statistical emulators, as surrogates of these simulators, can provide predictions of the physical process in a much faster and computationally inexpensive way. They can form a prominent solution to explore thousands of scenarios that would be otherwise numerically expensive and difficult to achieve. In this work, we build a statistical emulator of the deterministic codes used to simulate submarine sliding and tsunami generation at the Rockall Bank, NE Atlantic Ocean, in two stages. First we calibrate, against observations of the landslide deposits, the parameters used in the landslide simulations. This calibration is performed under a Bayesian framework using Gaussian Process (GP) emulators to approximate the landslide model, and the discrepancy function between model and observations. Distributions of the calibrated input parameters are obtained as a result of the calibration. In a second step, a GP emulator is built to mimic the coupled landslide-tsunami numerical process. The emulator propagates the uncertainties in the distributions of the calibrated input parameters inferred from the first step to the outputs. As a result, a quantification of the uncertainty of the maximum free surface elevation at specified locations is obtained. PMID:28484339

Statistical emulation of landslide-induced tsunamis at the Rockall Bank, NE Atlantic.

PubMed

Salmanidou, D M; Guillas, S; Georgiopoulou, A; Dias, F

2017-04-01

Statistical methods constitute a useful approach to understand and quantify the uncertainty that governs complex tsunami mechanisms. Numerical experiments may often have a high computational cost. This forms a limiting factor for performing uncertainty and sensitivity analyses, where numerous simulations are required. Statistical emulators, as surrogates of these simulators, can provide predictions of the physical process in a much faster and computationally inexpensive way. They can form a prominent solution to explore thousands of scenarios that would be otherwise numerically expensive and difficult to achieve. In this work, we build a statistical emulator of the deterministic codes used to simulate submarine sliding and tsunami generation at the Rockall Bank, NE Atlantic Ocean, in two stages. First we calibrate, against observations of the landslide deposits, the parameters used in the landslide simulations. This calibration is performed under a Bayesian framework using Gaussian Process (GP) emulators to approximate the landslide model, and the discrepancy function between model and observations. Distributions of the calibrated input parameters are obtained as a result of the calibration. In a second step, a GP emulator is built to mimic the coupled landslide-tsunami numerical process. The emulator propagates the uncertainties in the distributions of the calibrated input parameters inferred from the first step to the outputs. As a result, a quantification of the uncertainty of the maximum free surface elevation at specified locations is obtained.

An approach for the assessment of the statistical aspects of the SEA coupling loss factors and the vibrational energy transmission in complex aircraft structures: Experimental investigation and methods benchmark

NASA Astrophysics Data System (ADS)

Bouhaj, M.; von Estorff, O.; Peiffer, A.

2017-09-01

In the application of Statistical Energy Analysis "SEA" to complex assembled structures, a purely predictive model often exhibits errors. These errors are mainly due to a lack of accurate modelling of the power transmission mechanism described through the Coupling Loss Factors (CLF). Experimental SEA (ESEA) is practically used by the automotive and aerospace industry to verify and update the model or to derive the CLFs for use in an SEA predictive model when analytical estimates cannot be made. This work is particularly motivated by the lack of procedures that allow an estimate to be made of the variance and confidence intervals of the statistical quantities when using the ESEA technique. The aim of this paper is to introduce procedures enabling a statistical description of measured power input, vibration energies and the derived SEA parameters. Particular emphasis is placed on the identification of structural CLFs of complex built-up structures comparing different methods. By adopting a Stochastic Energy Model (SEM), the ensemble average in ESEA is also addressed. For this purpose, expressions are obtained to randomly perturb the energy matrix elements and generate individual samples for the Monte Carlo (MC) technique applied to derive the ensemble averaged CLF. From results of ESEA tests conducted on an aircraft fuselage section, the SEM approach provides a better performance of estimated CLFs compared to classical matrix inversion methods. The expected range of CLF values and the synthesized energy are used as quality criteria of the matrix inversion, allowing to assess critical SEA subsystems, which might require a more refined statistical description of the excitation and the response fields. Moreover, the impact of the variance of the normalized vibration energy on uncertainty of the derived CLFs is outlined.

An instructive model of entropy

NASA Astrophysics Data System (ADS)

Zimmerman, Seth

2010-09-01

This article first notes the misinterpretation of a common thought experiment, and the misleading comment that 'systems tend to flow from less probable to more probable macrostates'. It analyses the experiment, generalizes it and introduces a new tool of investigation, the simplectic structure. A time-symmetric model is built upon this structure, yielding several non-intuitive results. The approach is combinatorial rather than statistical, and assumes that entropy is equivalent to 'missing information'. The intention of this article is not only to present interesting results, but also, by deliberately starting with a simple example and developing it through proof and computer simulation, to clarify the often confusing subject of entropy. The article should be particularly stimulating to students and instructors of discrete mathematics or undergraduate physics.

Project-based introduction to aerospace engineering course: A model rocket

NASA Astrophysics Data System (ADS)

Jayaram, Sanjay; Boyer, Lawrence; George, John; Ravindra, K.; Mitchell, Kyle

2010-05-01

In this paper, a model rocket project suitable for sophomore aerospace engineering students is described. This project encompasses elements of drag estimation, thrust determination and analysis using digital data acquisition, statistical analysis of data, computer aided drafting, programming, team work and written communication skills. The student built rockets are launched in the university baseball field with the objective of carrying a specific amount of payload so that the rocket achieves a specific altitude before the parachute is deployed. During the course of the project, the students are introduced to real-world engineering practice through written report submission of their designs. Over the years, the project has proven to enhance the learning objectives, yet cost effective and has provided good outcome measures.

Acoustic environmental accuracy requirements for response determination

NASA Technical Reports Server (NTRS)

Pettitt, M. R.

1983-01-01

A general purpose computer program was developed for the prediction of vehicle interior noise. This program, named VIN, has both modal and statistical energy analysis capabilities for structural/acoustic interaction analysis. The analytic models and their computer implementation were verified through simple test cases with well-defined experimental results. The model was also applied in a space shuttle payload bay launch acoustics prediction study. The computer program processes large and small problems with equal efficiency because all arrays are dynamically sized by program input variables at run time. A data base is built and easily accessed for design studies. The data base significantly reduces the computational costs of such studies by allowing the reuse of the still-valid calculated parameters of previous iterations.

Empirical Research of Micro-blog Information Transmission Range by Guard nodes

NASA Astrophysics Data System (ADS)

Chen, Shan; Ji, Ling; Li, Guang

2018-03-01

The prediction and evaluation of information transmission in online social networks is a challenge. It is significant to solve this issue for monitoring public option and advertisement communication. First, the prediction process is described by a set language. Then with Sina Microblog system as used as the case object, the relationship between node influence and coverage rate is analyzed by using the topology structure of information nodes. A nonlinear model is built by a statistic method in a specific, bounded and controlled Microblog network. It can predict the message coverage rate by guard nodes. The experimental results show that the prediction model has higher accuracy to the source nodes which have lower influence in social network and practical application.

Development and validation of a climate-based ensemble prediction model for West Nile Virus infection rates in Culex mosquitoes, Suffolk County, New York.

PubMed

Little, Eliza; Campbell, Scott R; Shaman, Jeffrey

2016-08-09

West Nile Virus (WNV) is an endemic public health concern in the United States that produces periodic seasonal epidemics. Underlying these outbreaks is the enzootic cycle of WNV between mosquito vectors and bird hosts. Identifying the key environmental conditions that facilitate and accelerate this cycle can be used to inform effective vector control. Here, we model and forecast WNV infection rates among mosquito vectors in Suffolk County, New York using readily available meteorological and hydrological conditions. We first validate a statistical model built with surveillance data between 2001 and 2009 (m09) and specify a set of new statistical models using surveillance data from 2001 to 2012 (m12). This ensemble of new models is then used to make predictions for 2013-2015, and multimodel inference is employed to provide a formal probabilistic interpretation across the disparate individual model predictions. The findings of the m09 and m12 models align; with the ensemble of m12 models indicating an association between warm, dry early spring (April) conditions and increased annual WNV infection rates in Culex mosquitoes. This study shows that real-time climate information can be used to predict WNV infection rates in Culex mosquitoes prior to its seasonal peak and before WNV spillover transmission risk to humans is greatest.

Environmental Quality Index webinar

EPA Pesticide Factsheets

Environmental Quality index, data reduction approaches to help improve statistical efficiency, summarizing information on the wider environment humans are exposed to. air, water, land, built, socio-demographic, human and environmental health

Prediction of thrombophilia in patients with unexplained recurrent pregnancy loss using a statistical model.

PubMed

Wang, Tongfei; Kang, Xiaomin; He, Liying; Liu, Zhilan; Xu, Haijing; Zhao, Aimin

2017-09-01

To establish a statistical model to predict thrombophilia in patients with unexplained recurrent pregnancy loss (URPL). A retrospective case-control study was conducted at Ren Ji Hospital, Shanghai, China, from March 2014 to October 2016. The levels of D-dimer (DD), fibrinogen degradation products (FDP), activated partial thromboplastin time (APTT), prothrombin time (PT), thrombin time (TT), fibrinogen (Fg), and platelet aggregation in response to arachidonic acid (AA) and adenosine diphosphate (ADP) were collected. Receiver operating characteristic curve analysis was used to analyze data from 158 UPRL patients (≥3 previous first trimester pregnancy losses with unexplained etiology) and 131 non-RPL patients (no history of recurrent pregnancy loss). A logistic regression model (LRM) was built and the model was externally validated in another group of patients. The LRM included AA, DD, FDP, TT, APTT, and PT. The overall accuracy of the LRM was 80.9%, with sensitivity and specificity of 78.5% and 78.3%, respectively. The diagnostic threshold of the possibility of the LRM was 0.6492, with a sensitivity of 78.5% and a specificity of 78.3%. Subsequently, the LRM was validated with an overall accuracy of 83.6%. The LRM is a valuable model for prediction of thrombophilia in URPL patients. © 2017 International Federation of Gynecology and Obstetrics.

Retrieval of Atmospheric Particulate Matter Using Satellite Data Over Central and Eastern China

NASA Astrophysics Data System (ADS)

Chen, G. L.; Guang, J.; Li, Y.; Che, Y. H.; Gong, S. Q.

2018-04-01

Fine particulate matter (PM2.5) is a particle cluster with diameters less than or equal to 2.5 μm. Over the past few decades, regional air pollution composed of PM2.5 has frequently occurred over Central and Eastern China. In order to estimate the concentration, distribution and other properties of PM2.5, the general retrieval models built by establishing the relationship between aerosol optical depth (AOD) and PM2.5 has been widely used in many studies, including experimental models via statistics analysis and physical models with certain physical mechanism. The statistical experimental models can't be extended to other areas or historical period due to its dependence on the ground-based observations and necessary auxiliary data, which limits its further application. In this paper, a physically based model is applied to estimate the concentration of PM2.5 over Central and Eastern China from 2007 to 2016. The ground-based PM2.5 measurements were used to be as reference data to validate our retrieval results. Then annual variation and distribution of PM2.5 concentration in the Central and Eastern China was analysed. Results shows that the annual average PM2.5 show a trend of gradually increasing and then decreasing during 2007-2016, with the highest value in 2011.

Application of Linear Mixed-Effects Models in Human Neuroscience Research: A Comparison with Pearson Correlation in Two Auditory Electrophysiology Studies.

PubMed

Koerner, Tess K; Zhang, Yang

2017-02-27

Neurophysiological studies are often designed to examine relationships between measures from different testing conditions, time points, or analysis techniques within the same group of participants. Appropriate statistical techniques that can take into account repeated measures and multivariate predictor variables are integral and essential to successful data analysis and interpretation. This work implements and compares conventional Pearson correlations and linear mixed-effects (LME) regression models using data from two recently published auditory electrophysiology studies. For the specific research questions in both studies, the Pearson correlation test is inappropriate for determining strengths between the behavioral responses for speech-in-noise recognition and the multiple neurophysiological measures as the neural responses across listening conditions were simply treated as independent measures. In contrast, the LME models allow a systematic approach to incorporate both fixed-effect and random-effect terms to deal with the categorical grouping factor of listening conditions, between-subject baseline differences in the multiple measures, and the correlational structure among the predictor variables. Together, the comparative data demonstrate the advantages as well as the necessity to apply mixed-effects models to properly account for the built-in relationships among the multiple predictor variables, which has important implications for proper statistical modeling and interpretation of human behavior in terms of neural correlates and biomarkers.

Using open source computational tools for predicting human metabolic stability and additional absorption, distribution, metabolism, excretion, and toxicity properties.

PubMed

Gupta, Rishi R; Gifford, Eric M; Liston, Ted; Waller, Chris L; Hohman, Moses; Bunin, Barry A; Ekins, Sean

2010-11-01

Ligand-based computational models could be more readily shared between researchers and organizations if they were generated with open source molecular descriptors [e.g., chemistry development kit (CDK)] and modeling algorithms, because this would negate the requirement for proprietary commercial software. We initially evaluated open source descriptors and model building algorithms using a training set of approximately 50,000 molecules and a test set of approximately 25,000 molecules with human liver microsomal metabolic stability data. A C5.0 decision tree model demonstrated that CDK descriptors together with a set of Smiles Arbitrary Target Specification (SMARTS) keys had good statistics [κ = 0.43, sensitivity = 0.57, specificity = 0.91, and positive predicted value (PPV) = 0.64], equivalent to those of models built with commercial Molecular Operating Environment 2D (MOE2D) and the same set of SMARTS keys (κ = 0.43, sensitivity = 0.58, specificity = 0.91, and PPV = 0.63). Extending the dataset to ∼193,000 molecules and generating a continuous model using Cubist with a combination of CDK and SMARTS keys or MOE2D and SMARTS keys confirmed this observation. When the continuous predictions and actual values were binned to get a categorical score we observed a similar κ statistic (0.42). The same combination of descriptor set and modeling method was applied to passive permeability and P-glycoprotein efflux data with similar model testing statistics. In summary, open source tools demonstrated predictive results comparable to those of commercial software with attendant cost savings. We discuss the advantages and disadvantages of open source descriptors and the opportunity for their use as a tool for organizations to share data precompetitively, avoiding repetition and assisting drug discovery.

Covariation of depressive mood and spontaneous physical activity in major depressive disorder: toward continuous monitoring of depressive mood.

PubMed

Kim, Jinhyuk; Nakamura, Toru; Kikuchi, Hiroe; Yoshiuchi, Kazuhiro; Sasaki, Tsukasa; Yamamoto, Yoshiharu

2015-07-01

The objective evaluation of depressive mood is considered to be useful for the diagnosis and treatment of depressive disorders. Thus, we investigated psychobehavioral correlates, particularly the statistical associations between momentary depressive mood and behavioral dynamics measured objectively, in patients with major depressive disorder (MDD) and healthy subjects. Patients with MDD ( n = 14) and healthy subjects ( n = 43) wore a watch-type computer device and rated their momentary symptoms using ecological momentary assessment. Spontaneous physical activity in daily life, referred to as locomotor activity, was also continuously measured by an activity monitor built into the device. A multilevel modeling approach was used to model the associations between changes in depressive mood scores and the local statistics of locomotor activity simultaneously measured. We further examined the cross validity of such associations across groups. The statistical model established indicated that worsening of the depressive mood was associated with the increased intermittency of locomotor activity, as characterized by a lower mean and higher skewness. The model was cross validated across groups, suggesting that the same psychobehavioral correlates are shared by both healthy subjects and patients, although the latter had significantly higher mean levels of depressive mood scores. Our findings suggest the presence of robust as well as common associations between momentary depressive mood and behavioral dynamics in healthy individuals and patients with depression, which may lead to the continuous monitoring of the pathogenic processes (from healthy states) and pathological states of MDD.

Combining MEDLINE and publisher data to create parallel corpora for the automatic translation of biomedical text

PubMed Central

2013-01-01

Background Most of the institutional and research information in the biomedical domain is available in the form of English text. Even in countries where English is an official language, such as the United States, language can be a barrier for accessing biomedical information for non-native speakers. Recent progress in machine translation suggests that this technique could help make English texts accessible to speakers of other languages. However, the lack of adequate specialized corpora needed to train statistical models currently limits the quality of automatic translations in the biomedical domain. Results We show how a large-sized parallel corpus can automatically be obtained for the biomedical domain, using the MEDLINE database. The corpus generated in this work comprises article titles obtained from MEDLINE and abstract text automatically retrieved from journal websites, which substantially extends the corpora used in previous work. After assessing the quality of the corpus for two language pairs (English/French and English/Spanish) we use the Moses package to train a statistical machine translation model that outperforms previous models for automatic translation of biomedical text. Conclusions We have built translation data sets in the biomedical domain that can easily be extended to other languages available in MEDLINE. These sets can successfully be applied to train statistical machine translation models. While further progress should be made by incorporating out-of-domain corpora and domain-specific lexicons, we believe that this work improves the automatic translation of biomedical texts. PMID:23631733

Local-scale models reveal ecological niche variability in amphibian and reptile communities from two contrasting biogeographic regions

PubMed Central

Santos, Xavier; Felicísimo, Ángel M.

2016-01-01

Ecological Niche Models (ENMs) are widely used to describe how environmental factors influence species distribution. Modelling at a local scale, compared to a large scale within a high environmental gradient, can improve our understanding of ecological species niches. The main goal of this study is to assess and compare the contribution of environmental variables to amphibian and reptile ENMs in two Spanish national parks located in contrasting biogeographic regions, i.e., the Mediterranean and the Atlantic area. The ENMs were built with maximum entropy modelling using 11 environmental variables in each territory. The contributions of these variables to the models were analysed and classified using various statistical procedures (Mann–Whitney U tests, Principal Components Analysis and General Linear Models). Distance to the hydrological network was consistently the most relevant variable for both parks and taxonomic classes. Topographic variables (i.e., slope and altitude) were the second most predictive variables, followed by climatic variables. Differences in variable contribution were observed between parks and taxonomic classes. Variables related to water availability had the larger contribution to the models in the Mediterranean park, while topography variables were decisive in the Atlantic park. Specific response curves to environmental variables were in accordance with the biogeographic affinity of species (Mediterranean and non-Mediterranean species) and taxonomy (amphibians and reptiles). Interestingly, these results were observed for species located in both parks, particularly those situated at their range limits. Our findings show that ecological niche models built at local scale reveal differences in habitat preferences within a wide environmental gradient. Therefore, modelling at local scales rather than assuming large-scale models could be preferable for the establishment of conservation strategies for herptile species in natural parks. PMID:27761304

Point process models for localization and interdependence of punctate cellular structures.

PubMed

Li, Ying; Majarian, Timothy D; Naik, Armaghan W; Johnson, Gregory R; Murphy, Robert F

2016-07-01

Accurate representations of cellular organization for multiple eukaryotic cell types are required for creating predictive models of dynamic cellular function. To this end, we have previously developed the CellOrganizer platform, an open source system for generative modeling of cellular components from microscopy images. CellOrganizer models capture the inherent heterogeneity in the spatial distribution, size, and quantity of different components among a cell population. Furthermore, CellOrganizer can generate quantitatively realistic synthetic images that reflect the underlying cell population. A current focus of the project is to model the complex, interdependent nature of organelle localization. We built upon previous work on developing multiple non-parametric models of organelles or structures that show punctate patterns. The previous models described the relationships between the subcellular localization of puncta and the positions of cell and nuclear membranes and microtubules. We extend these models to consider the relationship to the endoplasmic reticulum (ER), and to consider the relationship between the positions of different puncta of the same type. Our results do not suggest that the punctate patterns we examined are dependent on ER position or inter- and intra-class proximity. With these results, we built classifiers to update previous assignments of proteins to one of 11 patterns in three distinct cell lines. Our generative models demonstrate the ability to construct statistically accurate representations of puncta localization from simple cellular markers in distinct cell types, capturing the complex phenomena of cellular structure interaction with little human input. This protocol represents a novel approach to vesicular protein annotation, a field that is often neglected in high-throughput microscopy. These results suggest that spatial point process models provide useful insight with respect to the spatial dependence between cellular structures. © 2016 International Society for Advancement of Cytometry. © 2016 International Society for Advancement of Cytometry.

Iterative model building, structure refinement and density modification with the PHENIX AutoBuild wizard

PubMed Central

Terwilliger, Thomas C.; Grosse-Kunstleve, Ralf W.; Afonine, Pavel V.; Moriarty, Nigel W.; Zwart, Peter H.; Hung, Li-Wei; Read, Randy J.; Adams, Paul D.

2008-01-01

The PHENIX AutoBuild wizard is a highly automated tool for iterative model building, structure refinement and density modification using RESOLVE model building, RESOLVE statistical density modification and phenix.refine structure refinement. Recent advances in the AutoBuild wizard and phenix.refine include automated detection and application of NCS from models as they are built, extensive model-completion algorithms and automated solvent-molecule picking. Model-completion algorithms in the AutoBuild wizard include loop building, crossovers between chains in different models of a structure and side-chain optimization. The AutoBuild wizard has been applied to a set of 48 structures at resolutions ranging from 1.1 to 3.2 Å, resulting in a mean R factor of 0.24 and a mean free R factor of 0.29. The R factor of the final model is dependent on the quality of the starting electron density and is relatively independent of resolution. PMID:18094468

The advancement of the built environment research through employment of structural equation modeling (SEM)

NASA Astrophysics Data System (ADS)

Wasilah, S.; Fahmyddin, T.

2018-03-01

The employment of structural equation modeling (SEM) in research has taken an increasing attention in among researchers in built environment. There is a gap to understand the attributes, application, and importance of this approach in data analysis in built environment study. This paper intends to provide fundamental comprehension of SEM method in data analysis, unveiling attributes, employment and significance and bestow cases to assess associations amongst variables and constructs. The study uses some main literature to grasp the essence of SEM regarding with built environment research. The better acknowledgment of this analytical tool may assist the researcher in the built environment to analyze data under complex research questions and to test multivariate models in a single study.

Mathematical Model of Cardiovascular and Metabolic Responses to Umbilical Cord Occlusions in Fetal Sheep.

PubMed

Wang, Qiming; Gold, Nathan; Frasch, Martin G; Huang, Huaxiong; Thiriet, Marc; Wang, Xiaogang

2015-12-01

Fetal acidemia during labor is associated with an increased risk of brain injury and lasting neurological deficits. This is in part due to the repetitive occlusions of the umbilical cord (UCO) induced by uterine contractions. Whereas fetal heart rate (FHR) monitoring is widely used clinically, it fails to detect fetal acidemia. Hence, new approaches are needed for early detection of fetal acidemia during labor. We built a mathematical model of the UCO effects on FHR, mean arterial blood pressure (MABP), oxygenation and metabolism. Mimicking fetal experiments, our in silico model reproduces salient features of experimentally observed fetal cardiovascular and metabolic behavior including FHR overshoot, gradual MABP decrease and mixed metabolic and respiratory acidemia during UCO. Combined with statistical analysis, our model provides valuable insight into the labor-like fetal distress and guidance for refining FHR monitoring algorithms to improve detection of fetal acidemia and cardiovascular decompensation.

Some predictions of the attached eddy model for a high Reynolds number boundary layer.

PubMed

Nickels, T B; Marusic, I; Hafez, S; Hutchins, N; Chong, M S

2007-03-15

Many flows of practical interest occur at high Reynolds number, at which the flow in most of the boundary layer is turbulent, showing apparently random fluctuations in velocity across a wide range of scales. The range of scales over which these fluctuations occur increases with the Reynolds number and hence high Reynolds number flows are difficult to compute or predict. In this paper, we discuss the structure of these flows and describe a physical model, based on the attached eddy hypothesis, which makes predictions for the statistical properties of these flows and their variation with Reynolds number. The predictions are shown to compare well with the results from recent experiments in a new purpose-built high Reynolds number facility. The model is also shown to provide a clear physical explanation for the trends in the data. The limits of applicability of the model are also discussed.

Mismatch between perceived and objectively measured environmental obesogenic features in European neighbourhoods.

PubMed

Roda, C; Charreire, H; Feuillet, T; Mackenbach, J D; Compernolle, S; Glonti, K; Ben Rebah, M; Bárdos, H; Rutter, H; McKee, M; De Bourdeaudhuij, I; Brug, J; Lakerveld, J; Oppert, J-M

2016-01-01

Findings from research on the association between the built environment and obesity remain equivocal but may be partly explained by differences in approaches used to characterize the built environment. Findings obtained using subjective measures may differ substantially from those measured objectively. We investigated the agreement between perceived and objectively measured obesogenic environmental features to assess (1) the extent of agreement between individual perceptions and observable characteristics of the environment and (2) the agreement between aggregated perceptions and observable characteristics, and whether this varied by type of characteristic, region or neighbourhood. Cross-sectional data from the SPOTLIGHT project (n = 6037 participants from 60 neighbourhoods in five European urban regions) were used. Residents' perceptions were self-reported, and objectively measured environmental features were obtained by a virtual audit using Google Street View. Percent agreement and Kappa statistics were calculated. The mismatch was quantified at neighbourhood level by a distance metric derived from a factor map. The extent to which the mismatch metric varied by region and neighbourhood was examined using linear regression models. Overall, agreement was moderate (agreement < 82%, kappa < 0.3) and varied by obesogenic environmental feature, region and neighbourhood. Highest agreement was found for food outlets and outdoor recreational facilities, and lowest agreement was obtained for aesthetics. In general, a better match was observed in high-residential density neighbourhoods characterized by a high density of food outlets and recreational facilities. Future studies should combine perceived and objectively measured built environment qualities to better understand the potential impact of the built environment on health, particularly in low residential density neighbourhoods. © 2016 World Obesity.

Maximum likelihood: Extracting unbiased information from complex networks

NASA Astrophysics Data System (ADS)

Garlaschelli, Diego; Loffredo, Maria I.

2008-07-01

The choice of free parameters in network models is subjective, since it depends on what topological properties are being monitored. However, we show that the maximum likelihood (ML) principle indicates a unique, statistically rigorous parameter choice, associated with a well-defined topological feature. We then find that, if the ML condition is incompatible with the built-in parameter choice, network models turn out to be intrinsically ill defined or biased. To overcome this problem, we construct a class of safely unbiased models. We also propose an extension of these results that leads to the fascinating possibility to extract, only from topological data, the “hidden variables” underlying network organization, making them “no longer hidden.” We test our method on World Trade Web data, where we recover the empirical gross domestic product using only topological information.

Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project.

PubMed

Gerstein, Mark B; Lu, Zhi John; Van Nostrand, Eric L; Cheng, Chao; Arshinoff, Bradley I; Liu, Tao; Yip, Kevin Y; Robilotto, Rebecca; Rechtsteiner, Andreas; Ikegami, Kohta; Alves, Pedro; Chateigner, Aurelien; Perry, Marc; Morris, Mitzi; Auerbach, Raymond K; Feng, Xin; Leng, Jing; Vielle, Anne; Niu, Wei; Rhrissorrakrai, Kahn; Agarwal, Ashish; Alexander, Roger P; Barber, Galt; Brdlik, Cathleen M; Brennan, Jennifer; Brouillet, Jeremy Jean; Carr, Adrian; Cheung, Ming-Sin; Clawson, Hiram; Contrino, Sergio; Dannenberg, Luke O; Dernburg, Abby F; Desai, Arshad; Dick, Lindsay; Dosé, Andréa C; Du, Jiang; Egelhofer, Thea; Ercan, Sevinc; Euskirchen, Ghia; Ewing, Brent; Feingold, Elise A; Gassmann, Reto; Good, Peter J; Green, Phil; Gullier, Francois; Gutwein, Michelle; Guyer, Mark S; Habegger, Lukas; Han, Ting; Henikoff, Jorja G; Henz, Stefan R; Hinrichs, Angie; Holster, Heather; Hyman, Tony; Iniguez, A Leo; Janette, Judith; Jensen, Morten; Kato, Masaomi; Kent, W James; Kephart, Ellen; Khivansara, Vishal; Khurana, Ekta; Kim, John K; Kolasinska-Zwierz, Paulina; Lai, Eric C; Latorre, Isabel; Leahey, Amber; Lewis, Suzanna; Lloyd, Paul; Lochovsky, Lucas; Lowdon, Rebecca F; Lubling, Yaniv; Lyne, Rachel; MacCoss, Michael; Mackowiak, Sebastian D; Mangone, Marco; McKay, Sheldon; Mecenas, Desirea; Merrihew, Gennifer; Miller, David M; Muroyama, Andrew; Murray, John I; Ooi, Siew-Loon; Pham, Hoang; Phippen, Taryn; Preston, Elicia A; Rajewsky, Nikolaus; Rätsch, Gunnar; Rosenbaum, Heidi; Rozowsky, Joel; Rutherford, Kim; Ruzanov, Peter; Sarov, Mihail; Sasidharan, Rajkumar; Sboner, Andrea; Scheid, Paul; Segal, Eran; Shin, Hyunjin; Shou, Chong; Slack, Frank J; Slightam, Cindie; Smith, Richard; Spencer, William C; Stinson, E O; Taing, Scott; Takasaki, Teruaki; Vafeados, Dionne; Voronina, Ksenia; Wang, Guilin; Washington, Nicole L; Whittle, Christina M; Wu, Beijing; Yan, Koon-Kiu; Zeller, Georg; Zha, Zheng; Zhong, Mei; Zhou, Xingliang; Ahringer, Julie; Strome, Susan; Gunsalus, Kristin C; Micklem, Gos; Liu, X Shirley; Reinke, Valerie; Kim, Stuart K; Hillier, LaDeana W; Henikoff, Steven; Piano, Fabio; Snyder, Michael; Stein, Lincoln; Lieb, Jason D; Waterston, Robert H

2010-12-24

We systematically generated large-scale data sets to improve genome annotation for the nematode Caenorhabditis elegans, a key model organism. These data sets include transcriptome profiling across a developmental time course, genome-wide identification of transcription factor-binding sites, and maps of chromatin organization. From this, we created more complete and accurate gene models, including alternative splice forms and candidate noncoding RNAs. We constructed hierarchical networks of transcription factor-binding and microRNA interactions and discovered chromosomal locations bound by an unusually large number of transcription factors. Different patterns of chromatin composition and histone modification were revealed between chromosome arms and centers, with similarly prominent differences between autosomes and the X chromosome. Integrating data types, we built statistical models relating chromatin, transcription factor binding, and gene expression. Overall, our analyses ascribed putative functions to most of the conserved genome.

Improving the performance of a filling line based on simulation

NASA Astrophysics Data System (ADS)

Jasiulewicz-Kaczmarek, M.; Bartkowiak, T.

2016-08-01

The paper describes the method of improving performance of a filling line based on simulation. This study concerns a production line that is located in a manufacturing centre of a FMCG company. A discrete event simulation model was built using data provided by maintenance data acquisition system. Two types of failures were identified in the system and were approximated using continuous statistical distributions. The model was validated taking into consideration line performance measures. A brief Pareto analysis of line failures was conducted to identify potential areas of improvement. Two improvements scenarios were proposed and tested via simulation. The outcome of the simulations were the bases of financial analysis. NPV and ROI values were calculated taking into account depreciation, profits, losses, current CIT rate and inflation. A validated simulation model can be a useful tool in maintenance decision-making process.

Can upstaging of ductal carcinoma in situ be predicted at biopsy by histologic and mammographic features?

NASA Astrophysics Data System (ADS)

Shi, Bibo; Grimm, Lars J.; Mazurowski, Maciej A.; Marks, Jeffrey R.; King, Lorraine M.; Maley, Carlo C.; Hwang, E. Shelley; Lo, Joseph Y.

2017-03-01

Reducing the overdiagnosis and overtreatment associated with ductal carcinoma in situ (DCIS) requires accurate prediction of the invasive potential at cancer screening. In this work, we investigated the utility of pre-operative histologic and mammographic features to predict upstaging of DCIS. The goal was to provide intentionally conservative baseline performance using readily available data from radiologists and pathologists and only linear models. We conducted a retrospective analysis on 99 patients with DCIS. Of those 25 were upstaged to invasive cancer at the time of definitive surgery. Pre-operative factors including both the histologic features extracted from stereotactic core needle biopsy (SCNB) reports and the mammographic features annotated by an expert breast radiologist were investigated with statistical analysis. Furthermore, we built classification models based on those features in an attempt to predict the presence of an occult invasive component in DCIS, with generalization performance assessed by receiver operating characteristic (ROC) curve analysis. Histologic features including nuclear grade and DCIS subtype did not show statistically significant differences between cases with pure DCIS and with DCIS plus invasive disease. However, three mammographic features, i.e., the major axis length of DCIS lesion, the BI-RADS level of suspicion, and radiologist's assessment did achieve the statistical significance. Using those three statistically significant features as input, a linear discriminant model was able to distinguish patients with DCIS plus invasive disease from those with pure DCIS, with AUC-ROC equal to 0.62. Overall, mammograms used for breast screening contain useful information that can be perceived by radiologists and help predict occult invasive components in DCIS.

Gbm.auto: A software tool to simplify spatial modelling and Marine Protected Area planning

PubMed Central

Officer, Rick; Clarke, Maurice; Reid, David G.; Brophy, Deirdre

2017-01-01

Boosted Regression Trees. Excellent for data-poor spatial management but hard to use Marine resource managers and scientists often advocate spatial approaches to manage data-poor species. Existing spatial prediction and management techniques are either insufficiently robust, struggle with sparse input data, or make suboptimal use of multiple explanatory variables. Boosted Regression Trees feature excellent performance and are well suited to modelling the distribution of data-limited species, but are extremely complicated and time-consuming to learn and use, hindering access for a wide potential user base and therefore limiting uptake and usage. BRTs automated and simplified for accessible general use with rich feature set We have built a software suite in R which integrates pre-existing functions with new tailor-made functions to automate the processing and predictive mapping of species abundance data: by automating and greatly simplifying Boosted Regression Tree spatial modelling, the gbm.auto R package suite makes this powerful statistical modelling technique more accessible to potential users in the ecological and modelling communities. The package and its documentation allow the user to generate maps of predicted abundance, visualise the representativeness of those abundance maps and to plot the relative influence of explanatory variables and their relationship to the response variables. Databases of the processed model objects and a report explaining all the steps taken within the model are also generated. The package includes a previously unavailable Decision Support Tool which combines estimated escapement biomass (the percentage of an exploited population which must be retained each year to conserve it) with the predicted abundance maps to generate maps showing the location and size of habitat that should be protected to conserve the target stocks (candidate MPAs), based on stakeholder priorities, such as the minimisation of fishing effort displacement. Gbm.auto for management in various settings By bridging the gap between advanced statistical methods for species distribution modelling and conservation science, management and policy, these tools can allow improved spatial abundance predictions, and therefore better management, decision-making, and conservation. Although this package was built to support spatial management of a data-limited marine elasmobranch fishery, it should be equally applicable to spatial abundance modelling, area protection, and stakeholder engagement in various scenarios. PMID:29216310

MAX UnMix: A web application for unmixing magnetic coercivity distributions

NASA Astrophysics Data System (ADS)

Maxbauer, Daniel P.; Feinberg, Joshua M.; Fox, David L.

2016-10-01

It is common in the fields of rock and environmental magnetism to unmix magnetic mineral components using statistical methods that decompose various types of magnetization curves (e.g., acquisition, demagnetization, or backfield). A number of programs have been developed over the past decade that are frequently used by the rock magnetic community, however many of these programs are either outdated or have obstacles inhibiting their usability. MAX UnMix is a web application (available online at http://www.irm.umn.edu/maxunmix), built using the shiny package for R studio, that can be used for unmixing coercivity distributions derived from magnetization curves. Here, we describe in detail the statistical model underpinning the MAX UnMix web application and discuss the programs functionality. MAX UnMix is an improvement over previous unmixing programs in that it is designed to be user friendly, runs as an independent website, and is platform independent.

Retrieval Capabilities of Hierarchical Networks: From Dyson to Hopfield

NASA Astrophysics Data System (ADS)

Agliari, Elena; Barra, Adriano; Galluzzi, Andrea; Guerra, Francesco; Tantari, Daniele; Tavani, Flavia

2015-01-01

We consider statistical-mechanics models for spin systems built on hierarchical structures, which provide a simple example of non-mean-field framework. We show that the coupling decay with spin distance can give rise to peculiar features and phase diagrams much richer than their mean-field counterpart. In particular, we consider the Dyson model, mimicking ferromagnetism in lattices, and we prove the existence of a number of metastabilities, beyond the ordered state, which become stable in the thermodynamic limit. Such a feature is retained when the hierarchical structure is coupled with the Hebb rule for learning, hence mimicking the modular architecture of neurons, and gives rise to an associative network able to perform single pattern retrieval as well as multiple-pattern retrieval, depending crucially on the external stimuli and on the rate of interaction decay with distance; however, those emergent multitasking features reduce the network capacity with respect to the mean-field counterpart. The analysis is accomplished through statistical mechanics, Markov chain theory, signal-to-noise ratio technique, and numerical simulations in full consistency. Our results shed light on the biological complexity shown by real networks, and suggest future directions for understanding more realistic models.

Exploring the Effects of Stellar Multiplicity on Exoplanet Occurrence Rates

NASA Astrophysics Data System (ADS)

Barclay, Thomas; Shabram, Megan

2017-06-01

Determining the frequency of habitable worlds is a key goal of the Kepler mission. During Kepler's four year investigation it detected thousands of transiting exoplanets with sizes varying from smaller than Mercury to larger than Jupiter. Finding planets was just the first step to determining frequency, and for the past few years the mission team has been modeling the reliability and completeness of the Kepler planet sample. One effect that has not typically been built into occurrence rate statistics is that of stellar multiplicity. If a planet orbits the primary star in a binary or triple star system then the transit depth will be somewhat diluted resulting in a modest underestimation in the planet size. However, if a detected planet orbits a fainter star then the error in measured planet radius can be very significant. We have taken a hypothetical star and planet population and passed that through a Kepler detection model. From this we have derived completeness corrections for a realistic case of a Universe with binary stars and compared that with a model Universe where all stars are single. We report on the impact that binaries have on exoplanet population statistics.

Kernel methods and flexible inference for complex stochastic dynamics

NASA Astrophysics Data System (ADS)

Capobianco, Enrico

2008-07-01

Approximation theory suggests that series expansions and projections represent standard tools for random process applications from both numerical and statistical standpoints. Such instruments emphasize the role of both sparsity and smoothness for compression purposes, the decorrelation power achieved in the expansion coefficients space compared to the signal space, and the reproducing kernel property when some special conditions are met. We consider these three aspects central to the discussion in this paper, and attempt to analyze the characteristics of some known approximation instruments employed in a complex application domain such as financial market time series. Volatility models are often built ad hoc, parametrically and through very sophisticated methodologies. But they can hardly deal with stochastic processes with regard to non-Gaussianity, covariance non-stationarity or complex dependence without paying a big price in terms of either model mis-specification or computational efficiency. It is thus a good idea to look at other more flexible inference tools; hence the strategy of combining greedy approximation and space dimensionality reduction techniques, which are less dependent on distributional assumptions and more targeted to achieve computationally efficient performances. Advantages and limitations of their use will be evaluated by looking at algorithmic and model building strategies, and by reporting statistical diagnostics.

Large-scale gene function analysis with the PANTHER classification system.

PubMed

Mi, Huaiyu; Muruganujan, Anushya; Casagrande, John T; Thomas, Paul D

2013-08-01

The PANTHER (protein annotation through evolutionary relationship) classification system (http://www.pantherdb.org/) is a comprehensive system that combines gene function, ontology, pathways and statistical analysis tools that enable biologists to analyze large-scale, genome-wide data from sequencing, proteomics or gene expression experiments. The system is built with 82 complete genomes organized into gene families and subfamilies, and their evolutionary relationships are captured in phylogenetic trees, multiple sequence alignments and statistical models (hidden Markov models or HMMs). Genes are classified according to their function in several different ways: families and subfamilies are annotated with ontology terms (Gene Ontology (GO) and PANTHER protein class), and sequences are assigned to PANTHER pathways. The PANTHER website includes a suite of tools that enable users to browse and query gene functions, and to analyze large-scale experimental data with a number of statistical tests. It is widely used by bench scientists, bioinformaticians, computer scientists and systems biologists. In the 2013 release of PANTHER (v.8.0), in addition to an update of the data content, we redesigned the website interface to improve both user experience and the system's analytical capability. This protocol provides a detailed description of how to analyze genome-wide experimental data with the PANTHER classification system.

Statistics of cosmic density profiles from perturbation theory

NASA Astrophysics Data System (ADS)

Bernardeau, Francis; Pichon, Christophe; Codis, Sandrine

2014-11-01

The joint probability distribution function (PDF) of the density within multiple concentric spherical cells is considered. It is shown how its cumulant generating function can be obtained at tree order in perturbation theory as the Legendre transform of a function directly built in terms of the initial moments. In the context of the upcoming generation of large-scale structure surveys, it is conjectured that this result correctly models such a function for finite values of the variance. Detailed consequences of this assumption are explored. In particular the corresponding one-cell density probability distribution at finite variance is computed for realistic power spectra, taking into account its scale variation. It is found to be in agreement with Λ -cold dark matter simulations at the few percent level for a wide range of density values and parameters. Related explicit analytic expansions at the low and high density tails are given. The conditional (at fixed density) and marginal probability of the slope—the density difference between adjacent cells—and its fluctuations is also computed from the two-cell joint PDF; it also compares very well to simulations. It is emphasized that this could prove useful when studying the statistical properties of voids as it can serve as a statistical indicator to test gravity models and/or probe key cosmological parameters.

On the Statistical Errors of RADAR Location Sensor Networks with Built-In Wi-Fi Gaussian Linear Fingerprints

PubMed Central

Zhou, Mu; Xu, Yu Bin; Ma, Lin; Tian, Shuo

2012-01-01

The expected errors of RADAR sensor networks with linear probabilistic location fingerprints inside buildings with varying Wi-Fi Gaussian strength are discussed. As far as we know, the statistical errors of equal and unequal-weighted RADAR networks have been suggested as a better way to evaluate the behavior of different system parameters and the deployment of reference points (RPs). However, up to now, there is still not enough related work on the relations between the statistical errors, system parameters, number and interval of the RPs, let alone calculating the correlated analytical expressions of concern. Therefore, in response to this compelling problem, under a simple linear distribution model, much attention will be paid to the mathematical relations of the linear expected errors, number of neighbors, number and interval of RPs, parameters in logarithmic attenuation model and variations of radio signal strength (RSS) at the test point (TP) with the purpose of constructing more practical and reliable RADAR location sensor networks (RLSNs) and also guaranteeing the accuracy requirements for the location based services in future ubiquitous context-awareness environments. Moreover, the numerical results and some real experimental evaluations of the error theories addressed in this paper will also be presented for our future extended analysis. PMID:22737027

On the statistical errors of RADAR location sensor networks with built-in Wi-Fi Gaussian linear fingerprints.

PubMed

Zhou, Mu; Xu, Yu Bin; Ma, Lin; Tian, Shuo

2012-01-01

The expected errors of RADAR sensor networks with linear probabilistic location fingerprints inside buildings with varying Wi-Fi Gaussian strength are discussed. As far as we know, the statistical errors of equal and unequal-weighted RADAR networks have been suggested as a better way to evaluate the behavior of different system parameters and the deployment of reference points (RPs). However, up to now, there is still not enough related work on the relations between the statistical errors, system parameters, number and interval of the RPs, let alone calculating the correlated analytical expressions of concern. Therefore, in response to this compelling problem, under a simple linear distribution model, much attention will be paid to the mathematical relations of the linear expected errors, number of neighbors, number and interval of RPs, parameters in logarithmic attenuation model and variations of radio signal strength (RSS) at the test point (TP) with the purpose of constructing more practical and reliable RADAR location sensor networks (RLSNs) and also guaranteeing the accuracy requirements for the location based services in future ubiquitous context-awareness environments. Moreover, the numerical results and some real experimental evaluations of the error theories addressed in this paper will also be presented for our future extended analysis.

A multi-object statistical atlas adaptive for deformable registration errors in anomalous medical image segmentation

NASA Astrophysics Data System (ADS)

Botter Martins, Samuel; Vallin Spina, Thiago; Yasuda, Clarissa; Falcão, Alexandre X.

2017-02-01

Statistical Atlases have played an important role towards automated medical image segmentation. However, a challenge has been to make the atlas more adaptable to possible errors in deformable registration of anomalous images, given that the body structures of interest for segmentation might present significant differences in shape and texture. Recently, deformable registration errors have been accounted by a method that locally translates the statistical atlas over the test image, after registration, and evaluates candidate objects from a delineation algorithm in order to choose the best one as final segmentation. In this paper, we improve its delineation algorithm and extend the model to be a multi-object statistical atlas, built from control images and adaptable to anomalous images, by incorporating a texture classifier. In order to provide a first proof of concept, we instantiate the new method for segmenting, object-by-object and all objects simultaneously, the left and right brain hemispheres, and the cerebellum, without the brainstem, and evaluate it on MRT1-images of epilepsy patients before and after brain surgery, which removed portions of the temporal lobe. The results show efficiency gain with statistically significant higher accuracy, using the mean Average Symmetric Surface Distance, with respect to the original approach.

Optical character recognition of handwritten Arabic using hidden Markov models

NASA Astrophysics Data System (ADS)

Aulama, Mohannad M.; Natsheh, Asem M.; Abandah, Gheith A.; Olama, Mohammed M.

2011-04-01

The problem of optical character recognition (OCR) of handwritten Arabic has not received a satisfactory solution yet. In this paper, an Arabic OCR algorithm is developed based on Hidden Markov Models (HMMs) combined with the Viterbi algorithm, which results in an improved and more robust recognition of characters at the sub-word level. Integrating the HMMs represents another step of the overall OCR trends being currently researched in the literature. The proposed approach exploits the structure of characters in the Arabic language in addition to their extracted features to achieve improved recognition rates. Useful statistical information of the Arabic language is initially extracted and then used to estimate the probabilistic parameters of the mathematical HMM. A new custom implementation of the HMM is developed in this study, where the transition matrix is built based on the collected large corpus, and the emission matrix is built based on the results obtained via the extracted character features. The recognition process is triggered using the Viterbi algorithm which employs the most probable sequence of sub-words. The model was implemented to recognize the sub-word unit of Arabic text raising the recognition rate from being linked to the worst recognition rate for any character to the overall structure of the Arabic language. Numerical results show that there is a potentially large recognition improvement by using the proposed algorithms.

Optical character recognition of handwritten Arabic using hidden Markov models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aulama, Mohannad M.; Natsheh, Asem M.; Abandah, Gheith A.

2011-01-01

The problem of optical character recognition (OCR) of handwritten Arabic has not received a satisfactory solution yet. In this paper, an Arabic OCR algorithm is developed based on Hidden Markov Models (HMMs) combined with the Viterbi algorithm, which results in an improved and more robust recognition of characters at the sub-word level. Integrating the HMMs represents another step of the overall OCR trends being currently researched in the literature. The proposed approach exploits the structure of characters in the Arabic language in addition to their extracted features to achieve improved recognition rates. Useful statistical information of the Arabic language ismore » initially extracted and then used to estimate the probabilistic parameters of the mathematical HMM. A new custom implementation of the HMM is developed in this study, where the transition matrix is built based on the collected large corpus, and the emission matrix is built based on the results obtained via the extracted character features. The recognition process is triggered using the Viterbi algorithm which employs the most probable sequence of sub-words. The model was implemented to recognize the sub-word unit of Arabic text raising the recognition rate from being linked to the worst recognition rate for any character to the overall structure of the Arabic language. Numerical results show that there is a potentially large recognition improvement by using the proposed algorithms.« less

Modeling pattern in collections of parameters

USGS Publications Warehouse

Link, W.A.

1999-01-01

Wildlife management is increasingly guided by analyses of large and complex datasets. The description of such datasets often requires a large number of parameters, among which certain patterns might be discernible. For example, one may consider a long-term study producing estimates of annual survival rates; of interest is the question whether these rates have declined through time. Several statistical methods exist for examining pattern in collections of parameters. Here, I argue for the superiority of 'random effects models' in which parameters are regarded as random variables, with distributions governed by 'hyperparameters' describing the patterns of interest. Unfortunately, implementation of random effects models is sometimes difficult. Ultrastructural models, in which the postulated pattern is built into the parameter structure of the original data analysis, are approximations to random effects models. However, this approximation is not completely satisfactory: failure to account for natural variation among parameters can lead to overstatement of the evidence for pattern among parameters. I describe quasi-likelihood methods that can be used to improve the approximation of random effects models by ultrastructural models.

Numerical study on statistical properties of speckle pattern in laser projection display based on human eye model

NASA Astrophysics Data System (ADS)

Cui, Zhe; Wang, Anting; Ma, Qianli; Ming, Hai

2013-12-01

In this paper, the laser speckle pattern on human retina for a laser projection display is simulated. By introducing a specific eye model `Indiana Eye', the statistical properties of the laser speckle are numerical investigated. The results show that the aberrations of human eye (mostly spherical and chromatic) will decrease the speckle contrast felt by people. When the wavelength of the laser source is 550 nm (green), people will feel the strongest speck pattern and the weakest when the wavelength is 450 nm (blue). Myopia and hyperopia will decrease the speckle contrast by introducing large spherical aberrations. Although aberration is good for speckle reduction, but it will degrade the imaging capability of the eye. The results show that laser source (650 nm) will have the best image quality on the retina. At last, we compare the human eye with an aberration-free imaging system. Both the speckle contrast and the image quality appear different behavior in these two imaging systems. The results are useful when a standardized measurement procedure for speckle contrast needs to be built.

Methods for identifying SNP interactions: a review on variations of Logic Regression, Random Forest and Bayesian logistic regression.

PubMed

Chen, Carla Chia-Ming; Schwender, Holger; Keith, Jonathan; Nunkesser, Robin; Mengersen, Kerrie; Macrossan, Paula

2011-01-01

Due to advancements in computational ability, enhanced technology and a reduction in the price of genotyping, more data are being generated for understanding genetic associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and modeling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming for Association Studies, and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection.

A statistical look at the retrieval of exoplanetary atmospheres of super Earths and giant planets

NASA Astrophysics Data System (ADS)

Rocchetto, Marco; Waldmann, Ingo Peter; Tinetti, Giovanna; Yurchenko, Sergey; Tennyson, Jonathan

2015-08-01

Over the past decades transit spectroscopy has become one of the pioneering methods to characterise exoplanetary atmospheres. With the increasing number of observations, and the advent of new ground and spaced based instruments, it is now crucial to find the most optimal and objective methodologies to interpret these data, and understand the information content they convey. This is particularly true for smaller and fainter super Earth type planets.In this conference we will present a new take on the spectral retrieval of transiting planets, with particular focus on super Earth atmospheres. TauREx (Waldmann et al. 2015a,b.) is a new line-by-line radiative transfer atmospheric retrieval framework for transmission and emission spectroscopy of exoplanetary atmospheres, optimised for hot Jupiters and super Earths. The code has been built from scratch with the ideas of scalability, flexibility and automation. This allows to run retrievals with minimum user input that can be scaled to large cluster computing. Priors on the number and types of molecules considered are automatically determined using a custom built pattern recognition algorithm able to identify the most likely absorbers/emitters in the exoplanetary spectra, minimising the human bias in selecting the major atmospheric constituents.Using these tools, we investigate the impact of signal to noise, spectral resolution and wavelength coverage on the retrievability of individual model parameters from transit spectra of super Earths, and put our models to test (Rocchetto et al. 2015). Characterisation of the atmospheres of super Earths through transit spectroscopy is paramount, as it can provide an indirect - and so far unique - way to probe the nature of these planets. For the first time we analyse in a systematic way large grids of spectra generated for different observing scenarios. We perform thousands of retrievals aimed to fully map the degeneracies and understand the statistics of current exoplanetary retrieval models, in the limiting signal-to-noise regime of super Earth observations.

Case study on prediction of remaining methane potential of landfilled municipal solid waste by statistical analysis of waste composition data.

PubMed

Sel, İlker; Çakmakcı, Mehmet; Özkaya, Bestamin; Suphi Altan, H

2016-10-01

Main objective of this study was to develop a statistical model for easier and faster Biochemical Methane Potential (BMP) prediction of landfilled municipal solid waste by analyzing waste composition of excavated samples from 12 sampling points and three waste depths representing different landfilling ages of closed and active sections of a sanitary landfill site located in İstanbul, Turkey. Results of Principal Component Analysis (PCA) were used as a decision support tool to evaluation and describe the waste composition variables. Four principal component were extracted describing 76% of data set variance. The most effective components were determined as PCB, PO, T, D, W, FM, moisture and BMP for the data set. Multiple Linear Regression (MLR) models were built by original compositional data and transformed data to determine differences. It was observed that even residual plots were better for transformed data the R(2) and Adjusted R(2) values were not improved significantly. The best preliminary BMP prediction models consisted of D, W, T and FM waste fractions for both versions of regressions. Adjusted R(2) values of the raw and transformed models were determined as 0.69 and 0.57, respectively. Copyright © 2016 Elsevier Ltd. All rights reserved.

Measuring the food and built environments in urban centres: reliability and validity of the EURO-PREVOB Community Questionnaire.

PubMed

Pomerleau, J; Knai, C; Foster, C; Rutter, H; Darmon, N; Derflerova Brazdova, Z; Hadziomeragic, A F; Pekcan, G; Pudule, I; Robertson, A; Brunner, E; Suhrcke, M; Gabrijelcic Blenkus, M; Lhotska, L; Maiani, G; Mistura, L; Lobstein, T; Martin, B W; Elinder, L S; Logstrup, S; Racioppi, F; McKee, M

2013-03-01

The authors designed an instrument to measure objectively aspects of the built and food environments in urban areas, the EURO-PREVOB Community Questionnaire, within the EU-funded project 'Tackling the social and economic determinants of nutrition and physical activity for the prevention of obesity across Europe' (EURO-PREVOB). This paper describes its development, reliability, validity, feasibility and relevance to public health and obesity research. The Community Questionnaire is designed to measure key aspects of the food and built environments in urban areas of varying levels of affluence or deprivation, within different countries. The questionnaire assesses (1) the food environment and (2) the built environment. Pilot tests of the EURO-PREVOB Community Questionnaire were conducted in five to 10 purposively sampled urban areas of different socio-economic status in each of Ankara, Brno, Marseille, Riga, and Sarajevo. Inter-rater reliability was compared between two pairs of fieldworkers in each city centre using three methods: inter-observer agreement (IOA), kappa statistics, and intraclass correlation coefficients (ICCs). Data were collected successfully in all five cities. Overall reliability of the EURO-PREVOB Community Questionnaire was excellent (inter-observer agreement (IOA) > 0.87; intraclass correlation coefficients (ICC)s > 0.91 and kappa statistics > 0.7. However, assessment of certain aspects of the quality of the built environment yielded slightly lower IOA coefficients than the quantitative aspects. The EURO-PREVOB Community Questionnaire was found to be a reliable and practical observational tool for measuring differences in community-level data on environmental factors that can impact on dietary intake and physical activity. The next step is to evaluate its predictive power by collecting behavioural and anthropometric data relevant to obesity and its determinants. Copyright © 2013 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

STS-121/Discovery: Imagery Quick-Look Briefing

NASA Technical Reports Server (NTRS)

2006-01-01

Kyle Herring (NASA Public Affairs) introduced Wayne Hale (Space Shuttle Program Manager) who stated that the imagery for the Space shuttle external tank showed the tank performed very well. Image analysis showed small pieces of foam falling off the rocket booster and external tank. There was no risk involved in these minor incidents. Statistical models were built to assist in risk analysis. The orbiter performed excellently. Wayne also provided some close-up pictures of small pieces of foam separating from the external tank during launching. He said the crew will also perform a 100% inspection of the heat shield. This flight showed great improvement over previous flights.

A Monte Carlo study of Weibull reliability analysis for space shuttle main engine components

NASA Technical Reports Server (NTRS)

Abernethy, K.

1986-01-01

The incorporation of a number of additional capabilities into an existing Weibull analysis computer program and the results of Monte Carlo computer simulation study to evaluate the usefulness of the Weibull methods using samples with a very small number of failures and extensive censoring are discussed. Since the censoring mechanism inherent in the Space Shuttle Main Engine (SSME) data is hard to analyze, it was decided to use a random censoring model, generating censoring times from a uniform probability distribution. Some of the statistical techniques and computer programs that are used in the SSME Weibull analysis are described. The methods documented in were supplemented by adding computer calculations of approximate (using iteractive methods) confidence intervals for several parameters of interest. These calculations are based on a likelihood ratio statistic which is asymptotically a chisquared statistic with one degree of freedom. The assumptions built into the computer simulations are described. The simulation program and the techniques used in it are described there also. Simulation results are tabulated for various combinations of Weibull shape parameters and the numbers of failures in the samples.

Evaluating the sources of water to wells: Three techniques for metamodeling of a groundwater flow model

USGS Publications Warehouse

Fienen, Michael N.; Nolan, Bernard T.; Feinstein, Daniel T.

2016-01-01

For decision support, the insights and predictive power of numerical process models can be hampered by insufficient expertise and computational resources required to evaluate system response to new stresses. An alternative is to emulate the process model with a statistical “metamodel.” Built on a dataset of collocated numerical model input and output, a groundwater flow model was emulated using a Bayesian Network, an Artificial neural network, and a Gradient Boosted Regression Tree. The response of interest was surface water depletion expressed as the source of water-to-wells. The results have application for managing allocation of groundwater. Each technique was tuned using cross validation and further evaluated using a held-out dataset. A numerical MODFLOW-USG model of the Lake Michigan Basin, USA, was used for the evaluation. The performance and interpretability of each technique was compared pointing to advantages of each technique. The metamodel can extend to unmodeled areas.

Predicting Intra-Urban Population Densities in Africa using SAR and Optical Remote Sensing Data

NASA Astrophysics Data System (ADS)

Linard, C.; Steele, J.; Forget, Y.; Lopez, J.; Shimoni, M.

2017-12-01

The population of Africa is predicted to double over the next 40 years, driving profound social, environmental and epidemiological changes within rapidly growing cities. Estimations of within-city variations in population density must be improved in order to take urban heterogeneities into account and better help urban research and decision making, especially for vulnerability and health assessments. Satellite remote sensing offers an effective solution for mapping settlements and monitoring urbanization at different spatial and temporal scales. In Africa, the urban landscape is covered by slums and small houses, where the heterogeneity is high and where the man-made materials are natural. Innovative methods that combine optical and SAR data are therefore necessary for improving settlement mapping and population density predictions. An automatic method was developed to estimate built-up densities using recent and archived optical and SAR data and a multi-temporal database of built-up densities was produced for 48 African cities. Geo-statistical methods were then used to study the relationships between census-derived population densities and satellite-derived built-up attributes. Best predictors were combined in a Random Forest framework in order to predict intra-urban variations in population density in any large African city. Models show significant improvement of our spatial understanding of urbanization and urban population distribution in Africa in comparison to the state of the art.

Modeling of polychromatic attenuation using computed tomography reconstructed images

NASA Technical Reports Server (NTRS)

Yan, C. H.; Whalen, R. T.; Beaupre, G. S.; Yen, S. Y.; Napel, S.

1999-01-01

This paper presents a procedure for estimating an accurate model of the CT imaging process including spectral effects. As raw projection data are typically unavailable to the end-user, we adopt a post-processing approach that utilizes the reconstructed images themselves. This approach includes errors from x-ray scatter and the nonidealities of the built-in soft tissue correction into the beam characteristics, which is crucial to beam hardening correction algorithms that are designed to be applied directly to CT reconstructed images. We formulate this approach as a quadratic programming problem and propose two different methods, dimension reduction and regularization, to overcome ill conditioning in the model. For the regularization method we use a statistical procedure, Cross Validation, to select the regularization parameter. We have constructed step-wedge phantoms to estimate the effective beam spectrum of a GE CT-I scanner. Using the derived spectrum, we computed the attenuation ratios for the wedge phantoms and found that the worst case modeling error is less than 3% of the corresponding attenuation ratio. We have also built two test (hybrid) phantoms to evaluate the effective spectrum. Based on these test phantoms, we have shown that the effective beam spectrum provides an accurate model for the CT imaging process. Last, we used a simple beam hardening correction experiment to demonstrate the effectiveness of the estimated beam profile for removing beam hardening artifacts. We hope that this estimation procedure will encourage more independent research on beam hardening corrections and will lead to the development of application-specific beam hardening correction algorithms.

Statistical downscaling of IPCC sea surface wind and wind energy predictions for U.S. east coastal ocean, Gulf of Mexico and Caribbean Sea

NASA Astrophysics Data System (ADS)

Yao, Zhigang; Xue, Zuo; He, Ruoying; Bao, Xianwen; Song, Jun

2016-08-01

A multivariate statistical downscaling method is developed to produce regional, high-resolution, coastal surface wind fields based on the IPCC global model predictions for the U.S. east coastal ocean, the Gulf of Mexico (GOM), and the Caribbean Sea. The statistical relationship is built upon linear regressions between the empirical orthogonal function (EOF) spaces of a cross- calibrated, multi-platform, multi-instrument ocean surface wind velocity dataset (predictand) and the global NCEP wind reanalysis (predictor) over a 10 year period from 2000 to 2009. The statistical relationship is validated before applications and its effectiveness is confirmed by the good agreement between downscaled wind fields based on the NCEP reanalysis and in-situ surface wind measured at 16 National Data Buoy Center (NDBC) buoys in the U.S. east coastal ocean and the GOM during 1992-1999. The predictand-predictor relationship is applied to IPCC GFDL model output (2.0°×2.5°) of downscaled coastal wind at 0.25°×0.25° resolution. The temporal and spatial variability of future predicted wind speeds and wind energy potential over the study region are further quantified. It is shown that wind speed and power would significantly be reduced in the high CO2 climate scenario offshore of the mid-Atlantic and northeast U.S., with the speed falling to one quarter of its original value.

How Much are Built Environments Changing, and Where?: Patterns of Change by Neighborhood Sociodemographic Characteristics across Seven U.S. Metropolitan Areas

PubMed Central

Hirsch, Jana A.; Grengs, Joe; Schulz, Amy; Adar, Sara D.; Rodriguez, Daniel A.; Brines, Shannon J.; Diez Roux, Ana V.

2016-01-01

Investments in neighborhood built environments could increase physical activity and overall health. Disproportionate distribution of these changes in advantaged neighborhoods could inflate health disparities. Little information exists on where changes are occurring. This paper aims to 1) identify changes in the built environment in neighborhoods and 2) investigate associations between high levels of change and sociodemographic characteristics. Using Geographic Information Systems, neighborhood land-use, local destinations (for walking, social engagement, and physical activity), and sociodemographics were characterized in 2000 and 2010 for seven U.S. cities. Linear and change on change models estimated associations of built environment changes with baseline (2000) and change (2010–2000) in sociodemographics. Spatial patterns were assessed using Global Moran’s I to measure overall clustering of change and Local Moran’s I to identify statistically significant clusters of high increases surrounded by high increases (HH). Sociodemographic characteristics were compared between HH cluster and other tracts using Analysis of Variance (ANOVA). We observed small land-use changes but increases in the destination types. Greater increases in destinations were associated with higher percentage non-Hispanic whites, percentage households with no vehicle, and median household income. Associations were present for both baseline sociodemographics and changes over time. Greater increases in destinations were associated with lower baseline percentage over 65 but higher increases in percentage over 65 between 2000 and 2010. Global Moran’s indicated changes were spatially clustered. HH cluster tracts started with a higher percentage non-Hispanic whites and higher percentage of households without vehicles. Between 2000 and 2010, HH cluster tracts experienced increases in percent non-Hispanic white, greater increases in median household income, and larger decreases in percent of households without a vehicle. Changes in the built environment are occurring in neighborhoods across a diverse set of U.S. metropolitan areas, but are patterned such that they may lead to increased health disparities over time. PMID:27701020

What is Urban? A study of census and satellite-derived urban classes in the United States (1990-2010) with comparisons to India and Mexico

NASA Astrophysics Data System (ADS)

Balk, D.; Leyk, S.; Jones, B.; Clark, A.; Montgomery, M.

2017-12-01

Geographers and demographers have contributed much to understanding urban population and urban place. Yet, we nevertheless remain ill-prepared to fully understand past urban processes and our urban future, and importantly, connect that knowledge to pressing concerns such as climate and environmental change. This is largely due to well-known data limitations and inherent inconsistencies in the urban definition across countries and over time and spatial scales, and because urban models and definitions arise out of disciplinary silos. This paper provides a new framework for urban inquiry in that it combines urban definitions used by the U.S. Census Bureau from 1990-2010 with newly available satellite-based (mostly Landsat) data on built-up area from the Global Human Settlement Layer (GHSL). We identify areas of agreement and disagreement, as well as the population distribution underlying various GHSL derived built-up land thresholds. Our analysis allows for a systematic means of discerning peri-urban areas from other types of urban development, as well as examines differences in these patterns at the national and Metropolitan Statistical Area (MSA)-level. While we find overwhelming areas of agreement - about 70% of the census-designated urban population can be characterized as living on land that is at least 50% built-up - we also learn much of the significant heterogeneity in levels and patterns of growth between different MSAs. We further compare the US results with those for India and Mexico. This research unlocks the potential of such alternative measures for creating globally and temporally consistent proxies of urban land and may guide further research on consistent modeling of spatial demographic urban change, highly urgent for future work to distinguish between fine-scale levels of urban development and to forecast urban expansion.

Statistical wiring of thalamic receptive fields optimizes spatial sampling of the retinal image

PubMed Central

Wang, Xin; Sommer, Friedrich T.; Hirsch, Judith A.

2014-01-01

Summary It is widely assumed that mosaics of retinal ganglion cells establish the optimal representation of visual space. However, relay cells in the visual thalamus often receive convergent input from several retinal afferents and, in cat, outnumber ganglion cells. To explore how the thalamus transforms the retinal image, we built a model of the retinothalamic circuit using experimental data and simple wiring rules. The model shows how the thalamus might form a resampled map of visual space with the potential to facilitate detection of stimulus position in the presence of sensor noise. Bayesian decoding conducted with the model provides support for this scenario. Despite its benefits, however, resampling introduces image blur, thus impairing edge perception. Whole-cell recordings obtained in vivo suggest that this problem is mitigated by arrangements of excitation and inhibition within the receptive field that effectively boost contrast borders, much like strategies used in digital image processing. PMID:24559681

[Simulation and data mining model for identifying and prediction budget changes in the care of patients with hypertension].

PubMed

Joyanes-Aguilar, Luis; Castaño, Néstor J; Osorio, José H

2015-10-01

Objective To present a simulation model that establishes the economic impact to the health care system produced by the diagnostic evolution of patients suffering from arterial hypertension. Methodology The information used corresponds to that available in Individual Health Records (RIPs, in Spanish). A statistical characterization was carried out and a model for matrix storage in MATLAB was proposed. Data mining was used to create predictors. Finally, a simulation environment was built to determine the economic cost of diagnostic evolution. Results 5.7 % of the population progresses from the diagnosis, and the cost overrun associated with it is 43.2 %. Conclusions Results shows the applicability and possibility of focussing research on establishing diagnosis relationships using all the information reported in the RIPS in order to create econometric indicators that can determine which diagnostic evolutions are most relevant to budget allocation.

Prediction of anticancer activity of diterpenes isolated from the paraiban flora through a PLS model and molecular surfaces.

PubMed

Scotti, Luciana; Scotti, Marcus T; Ishiki, Hamilton; Junior, Francisco J B M; dos, Santos Paula F; Tavares, Josean F; da Silva, Marcelo S

2014-05-01

The aim of this work was to predict the anticancer potential of 3 atisane, and 3 trachylobane diterpene compounds extracted from the roots of Xylopia langsdorffiana. The prediction of anticancer activity as expressed against PC-3 tumor cells was made using a PLS model built with 26 diterpenes in the training set. Significant statistical measures were obtained. The six investigated diterpenes were applied to the model and their activities against PC-3 cells were calculated. All the diterpenes were active, with atisane diterpenes showing the higher pICso values. In human prostate carcinoma PC-3 cells, the apoptosis mechanism is related to an inhibition of IKK/NF-KB. Antioxidant potential implies a greater electronic molecular atmosphere (increased donor electron capacity), which can reduce radical reactivity, and facilitate post donation charge accommodation. Molecular surfaces indicated a much greater electronic cloud over atisane diterpenes.

Optimal descriptor as a translator of eclectic data into endpoint prediction: mutagenicity of fullerene as a mathematical function of conditions.

PubMed

Toropov, Andrey A; Toropova, Alla P

2014-06-01

The experimental data on the bacterial reverse mutation test on C60 nanoparticles (TA100) is examined as an endpoint. By means of the optimal descriptors calculated with the Monte Carlo method a mathematical model of the endpoint has been built up. The model is the mathematical function of (i) dose (g/plate); (ii) metabolic activation (i.e. with S9 mix or without S9 mix); and (iii) illumination (i.e. dark or irradiation). The statistical quality of the model is the following: n=10, r(2)=0.7549, q(2)=0.5709, s=7.67, F=25 (Training set); n=5, r(2)=0.8987, s=18.4 (Calibration set); and n=5, r(2)=0.6968, s=10.9 (Validation set). Copyright © 2013 Elsevier Ltd. All rights reserved.

Estimation of trabecular bone parameters in children from multisequence MRI using texture-based regression

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lekadir, Karim, E-mail: karim.lekadir@upf.edu; Hoogendoorn, Corné; Armitage, Paul

Purpose: This paper presents a statistical approach for the prediction of trabecular bone parameters from low-resolution multisequence magnetic resonance imaging (MRI) in children, thus addressing the limitations of high-resolution modalities such as HR-pQCT, including the significant exposure of young patients to radiation and the limited applicability of such modalities to peripheral bones in vivo. Methods: A statistical predictive model is constructed from a database of MRI and HR-pQCT datasets, to relate the low-resolution MRI appearance in the cancellous bone to the trabecular parameters extracted from the high-resolution images. The description of the MRI appearance is achieved between subjects by usingmore » a collection of feature descriptors, which describe the texture properties inside the cancellous bone, and which are invariant to the geometry and size of the trabecular areas. The predictive model is built by fitting to the training data a nonlinear partial least square regression between the input MRI features and the output trabecular parameters. Results: Detailed validation based on a sample of 96 datasets shows correlations >0.7 between the trabecular parameters predicted from low-resolution multisequence MRI based on the proposed statistical model and the values extracted from high-resolution HRp-QCT. Conclusions: The obtained results indicate the promise of the proposed predictive technique for the estimation of trabecular parameters in children from multisequence MRI, thus reducing the need for high-resolution radiation-based scans for a fragile population that is under development and growth.« less

The Role of Feature Selection and Statistical Weighting in ...

EPA Pesticide Factsheets

Our study assesses the value of both in vitro assay and quantitative structure activity relationship (QSAR) data in predicting in vivo toxicity using numerous statistical models and approaches to process the data. Our models are built on datasets of (i) 586 chemicals for which both in vitro and in vivo data are currently available in EPA’s Toxcast and ToxRefDB databases, respectively, and (ii) 769 chemicals for which both QSAR data and in vivo data exist. Similar to a previous study (based on just 309 chemicals, Thomas et al. 2012), after converting the continuous values from each dataset to binary values, the majority of more than 1,000 in vivo endpoints are poorly predicted. Even for the endpoints that are well predicted (about 40 with an F1 score of >0.75), imbalances in in vivo endpoint data or cytotoxicity across in vitro assays may be skewing results. In order to better account for these types of considerations, we examine best practices in data preprocessing and model fitting in real-world contexts where data are rife with imperfections. We discuss options for dealing with missing data, including omitting observations, aggregating variables, and imputing values. We also examine the impacts of feature selection (from both a statistical and biological perspective) on performance and efficiency, and we weight outcome data to reduce endpoint imbalances to account for potential chemical selection bias and assess revised performance. For example, initial weig

Simulation on a car interior aerodynamic noise control based on statistical energy analysis

NASA Astrophysics Data System (ADS)

Chen, Xin; Wang, Dengfeng; Ma, Zhengdong

2012-09-01

How to simulate interior aerodynamic noise accurately is an important question of a car interior noise reduction. The unsteady aerodynamic pressure on body surfaces is proved to be the key effect factor of car interior aerodynamic noise control in high frequency on high speed. In this paper, a detail statistical energy analysis (SEA) model is built. And the vibra-acoustic power inputs are loaded on the model for the valid result of car interior noise analysis. The model is the solid foundation for further optimization on car interior noise control. After the most sensitive subsystems for the power contribution to car interior noise are pointed by SEA comprehensive analysis, the sound pressure level of car interior aerodynamic noise can be reduced by improving their sound and damping characteristics. The further vehicle testing results show that it is available to improve the interior acoustic performance by using detailed SEA model, which comprised by more than 80 subsystems, with the unsteady aerodynamic pressure calculation on body surfaces and the materials improvement of sound/damping properties. It is able to acquire more than 2 dB reduction on the central frequency in the spectrum over 800 Hz. The proposed optimization method can be looked as a reference of car interior aerodynamic noise control by the detail SEA model integrated unsteady computational fluid dynamics (CFD) and sensitivity analysis of acoustic contribution.

Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't

PubMed Central

Roudi, Yasser; Nirenberg, Sheila; Latham, Peter E.

2009-01-01

One of the most critical problems we face in the study of biological systems is building accurate statistical descriptions of them. This problem has been particularly challenging because biological systems typically contain large numbers of interacting elements, which precludes the use of standard brute force approaches. Recently, though, several groups have reported that there may be an alternate strategy. The reports show that reliable statistical models can be built without knowledge of all the interactions in a system; instead, pairwise interactions can suffice. These findings, however, are based on the analysis of small subsystems. Here, we ask whether the observations will generalize to systems of realistic size, that is, whether pairwise models will provide reliable descriptions of true biological systems. Our results show that, in most cases, they will not. The reason is that there is a crossover in the predictive power of pairwise models: If the size of the subsystem is below the crossover point, then the results have no predictive power for large systems. If the size is above the crossover point, then the results may have predictive power. This work thus provides a general framework for determining the extent to which pairwise models can be used to predict the behavior of large biological systems. Applied to neural data, the size of most systems studied so far is below the crossover point. PMID:19424487

Application of Linear Mixed-Effects Models in Human Neuroscience Research: A Comparison with Pearson Correlation in Two Auditory Electrophysiology Studies

PubMed Central

Koerner, Tess K.; Zhang, Yang

2017-01-01

Neurophysiological studies are often designed to examine relationships between measures from different testing conditions, time points, or analysis techniques within the same group of participants. Appropriate statistical techniques that can take into account repeated measures and multivariate predictor variables are integral and essential to successful data analysis and interpretation. This work implements and compares conventional Pearson correlations and linear mixed-effects (LME) regression models using data from two recently published auditory electrophysiology studies. For the specific research questions in both studies, the Pearson correlation test is inappropriate for determining strengths between the behavioral responses for speech-in-noise recognition and the multiple neurophysiological measures as the neural responses across listening conditions were simply treated as independent measures. In contrast, the LME models allow a systematic approach to incorporate both fixed-effect and random-effect terms to deal with the categorical grouping factor of listening conditions, between-subject baseline differences in the multiple measures, and the correlational structure among the predictor variables. Together, the comparative data demonstrate the advantages as well as the necessity to apply mixed-effects models to properly account for the built-in relationships among the multiple predictor variables, which has important implications for proper statistical modeling and interpretation of human behavior in terms of neural correlates and biomarkers. PMID:28264422

Collaborative filtering on a family of biological targets.

PubMed

Erhan, Dumitru; L'heureux, Pierre-Jean; Yue, Shi Yi; Bengio, Yoshua

2006-01-01

Building a QSAR model of a new biological target for which few screening data are available is a statistical challenge. However, the new target may be part of a bigger family, for which we have more screening data. Collaborative filtering or, more generally, multi-task learning, is a machine learning approach that improves the generalization performance of an algorithm by using information from related tasks as an inductive bias. We use collaborative filtering techniques for building predictive models that link multiple targets to multiple examples. The more commonalities between the targets, the better the multi-target model that can be built. We show an example of a multi-target neural network that can use family information to produce a predictive model of an undersampled target. We evaluate JRank, a kernel-based method designed for collaborative filtering. We show their performance on compound prioritization for an HTS campaign and the underlying shared representation between targets. JRank outperformed the neural network both in the single- and multi-target models.

Machine learning modeling of plant phenology based on coupling satellite and gridded meteorological dataset

NASA Astrophysics Data System (ADS)

Czernecki, Bartosz; Nowosad, Jakub; Jabłońska, Katarzyna

2018-04-01

Changes in the timing of plant phenological phases are important proxies in contemporary climate research. However, most of the commonly used traditional phenological observations do not give any coherent spatial information. While consistent spatial data can be obtained from airborne sensors and preprocessed gridded meteorological data, not many studies robustly benefit from these data sources. Therefore, the main aim of this study is to create and evaluate different statistical models for reconstructing, predicting, and improving quality of phenological phases monitoring with the use of satellite and meteorological products. A quality-controlled dataset of the 13 BBCH plant phenophases in Poland was collected for the period 2007-2014. For each phenophase, statistical models were built using the most commonly applied regression-based machine learning techniques, such as multiple linear regression, lasso, principal component regression, generalized boosted models, and random forest. The quality of the models was estimated using a k-fold cross-validation. The obtained results showed varying potential for coupling meteorological derived indices with remote sensing products in terms of phenological modeling; however, application of both data sources improves models' accuracy from 0.6 to 4.6 day in terms of obtained RMSE. It is shown that a robust prediction of early phenological phases is mostly related to meteorological indices, whereas for autumn phenophases, there is a stronger information signal provided by satellite-derived vegetation metrics. Choosing a specific set of predictors and applying a robust preprocessing procedures is more important for final results than the selection of a particular statistical model. The average RMSE for the best models of all phenophases is 6.3, while the individual RMSE vary seasonally from 3.5 to 10 days. Models give reliable proxy for ground observations with RMSE below 5 days for early spring and late spring phenophases. For other phenophases, RMSE are higher and rise up to 9-10 days in the case of the earliest spring phenophases.

Modelled spatiotemporal variability of outdoor thermal comfort in local climate zones of the city of Brno, Czech Republic.

PubMed

Geletič, Jan; Lehnert, Michal; Savić, Stevan; Milošević, Dragan

2018-05-15

This study uses the MUKLIMO_3 urban climate model (in German, Mikroskaliges Urbanes KLImaMOdell in 3-Dimensionen) and measurements from an urban climate network in order to simulate, validate and analyse the spatiotemporal pattern of human thermal comfort outdoors in the city of Brno (Czech Republic) during a heat-wave period. HUMIDEX, a heat index designed to quantify human heat exposure, was employed to assess thermal comfort, employing air temperature and relative humidity data. The city was divided into local climate zones (LCZs) in order to access differences in intra-urban thermal comfort. Validation of the model results, based on the measurement dates within the urban monitoring network, confirmed that the MUKLIMO_3 micro-scale model had the capacity to simulate the main spatiotemporal patterns of thermal comfort in an urban area and its vicinity. The results suggested that statistically significant differences in outdoor thermal comfort exist in the majority of cases between different LCZs. The most built-up LCZ types (LCZs 2, 3, 5, 8 and 10) were disclosed as the most uncomfortable areas of the city. Hence, conditions of great discomfort (HUMIDEX >40) were recorded in these areas, mainly in the afternoon hours (from 13.00 to 18.00 CEST), while some thermal discomfort continued overnight. In contrast, HUMIDEX values in sparsely built-up LCZ 9 and non-urban LCZs were substantially lower and indicated better thermal conditions for the urban population. Interestingly, the model captured a local increase of HUMIDEX values arising out of air humidity in LCZs with the presence of more vegetation (LCZs A and B) and in the vicinity of larger bodies of water (LCZ G). Copyright © 2017 Elsevier B.V. All rights reserved.

Supplantation versus Generative Models: Implications for Designers of Instructional Text.

ERIC Educational Resources Information Center

Smith, Patricia L.

Two instructional design alternatives are described and discussed: (1) the supplantation model of Ausburn and Ausburn (1978), where learning strategies are built into the instructional materials; and (2) a generative design model, where strategies are "built" into the learner. These contrasting models are proposed as representing the…

Additive Interaction between Heterogeneous Environmental Quality Domains (Air, Water, Land, Sociodemographic, and Built Environment) on Preterm Birth.

PubMed

Grabich, Shannon C; Rappazzo, Kristen M; Gray, Christine L; Jagai, Jyotsna S; Jian, Yun; Messer, Lynne C; Lobdell, Danelle T

2016-01-01

Environmental exposures often occur in tandem; however, epidemiological research often focuses on singular exposures. Statistical interactions among broad, well-characterized environmental domains have not yet been evaluated in association with health. We address this gap by conducting a county-level cross-sectional analysis of interactions between Environmental Quality Index (EQI) domain indices on preterm birth in the Unites States from 2000 to 2005. The EQI, a county-level index constructed for the 2000-2005 time period, was constructed from five domain-specific indices (air, water, land, built, and sociodemographic) using principal component analyses. County-level preterm birth rates ( n  = 3141) were estimated using live births from the National Center for Health Statistics. Linear regression was used to estimate prevalence differences (PDs) and 95% confidence intervals (CIs) comparing worse environmental quality to the better quality for each model for (a) each individual domain main effect, (b) the interaction contrast, and (c) the two main effects plus interaction effect (i.e., the "net effect") to show departure from additivity for the all U.S. counties. Analyses were also performed for subgroupings by four urban/rural strata. We found the suggestion of antagonistic interactions but no synergism, along with several purely additive (i.e., no interaction) associations. In the non-stratified model, we observed antagonistic interactions, between the sociodemographic/air domains [net effect (i.e., the association, including main effects and interaction effects) PD: -0.004 (95% CI: -0.007, 0.000), interaction contrast: -0.013 (95% CI: -0.020, -0.007)] and built/air domains [net effect PD: 0.008 (95% CI 0.004, 0.011), interaction contrast: -0.008 (95% CI: -0.015, -0.002)]. Most interactions were between the air domain and other respective domains. Interactions differed by urbanicity, with more interactions observed in non-metropolitan regions. Observed antagonistic associations may indicate that those living in areas with multiple detrimental domains may have other interfering factors reducing the burden of environmental exposure. This study is the first to explore interactions across different environmental domains and demonstrates the utility of the EQI to examine the relationship between environmental domain interactions and human health. While we did observe some departures from additivity, many observed effects were additive. This study demonstrated that interactions between environmental domains should be considered in future analyses.

Surface temperature statistics over Los Angeles - The influence of land use

NASA Technical Reports Server (NTRS)

Dousset, Benedicte

1991-01-01

Surface temperature statistics from 84 NOAA AVHRR (Advanced Very High Resolution Radiometer) satellite images of the Los Angeles basin are interpreted as functions of the corresponding urban land-cover classified from a multispectral SPOT image. Urban heat islands observed in the temperature statistics correlate well with the distribution of industrial and fully built areas. Small cool islands coincide with highly watered parks and golf courses. There is a significant negative correlation between the afternoon surface temperature and a vegetation index computed from the SPOT image.

Pharmaceutical solid-state kinetic stability investigation by using moisture-modified Arrhenius equation and JMP statistical software.

PubMed

Fu, Mingkun; Perlman, Michael; Lu, Qing; Varga, Csanad

2015-03-25

An accelerated stress approach utilizing the moisture-modified Arrhenius equation and JMP statistical software was utilized to quantitatively assess the solid state stability of an investigational oncology drug MLNA under the influence of temperature (1/T) and humidity (%RH). Physical stability of MLNA under stress conditions was evaluated by using XRPD, DSC, TGA, and DVS, while chemical stability was evaluated by using HPLC. The major chemical degradation product was identified as a hydrolysis product of MLNA drug substance, and was subsequently subjected to an investigation of kinetics based on the isoconversion concept. A mathematical model (ln k=-11,991×(1/T)+0.0298×(%RH)+29.8823) based on the initial linear kinetics observed for the formation of this degradant at all seven stress conditions was built by using the moisture-modified Arrhenius equation and JMP statistical software. Comparison of the predicted versus experimental lnk values gave a mean deviation value of 5.8%, an R(2) value of 0.94, a p-value of 0.0038, and a coefficient of variation of the root mean square error CV(RMSE) of 7.9%. These statistics all indicated a good fit to the model for the stress data of MLNA. Both temperature and humidity were shown to have a statistically significant impact on stability by using effect leverage plots (p-value<0.05 for both 1/T and %RH). Inclusion of a term representing the interaction of relative humidity and temperature (%RH×1/T) was shown not to be justified by using Analysis of Covariance (ANCOVA), which supported the use of the moisture-corrected Arrhenius equation modeling theory. The model was found to be of value to aid setting of specifications and retest period, and storage condition selection. A model was also generated using only four conditions, as an example from a resource saving perspective, which was found to provide a good fit to the entire set of data. Copyright © 2015 Elsevier B.V. All rights reserved.

The effects of built environment attributes on physical activity-related health and health care costs outcomes in Australia.

PubMed

Zapata-Diomedi, Belen; Herrera, Ana Maria Mantilla; Veerman, J Lennert

2016-11-01

Attributes of the built environment can positively influence physical activity of urban populations, which results in health and economic benefits. In this study, we derived scenarios from the literature for the association built environment-physical activity and used a mathematical model to translate improvements in physical activity to health-adjusted life years and health care costs. We modelled 28 scenarios representing a diverse range of built environment attributes including density, diversity of land use, availability of destinations, distance to transit, design and neighbourhood walkability. Our results indicated potential health gains in 24 of the 28 modelled built environment attributes. Health care cost savings due to prevented physical activity-related diseases ranged between A$1300 to A$105,355 per 100,000 adults per year. On the other hand, additional health care costs of prolonged life years attributable to improvements in physical activity were nearly 50% higher than the estimated health care costs savings. Our results give an indication of the potential health benefits of investing in physical activity-friendly built environments. Copyright © 2016 Elsevier Ltd. All rights reserved.

Support Provided to the External Tank (ET) Project on the Use of Statistical Analysis for ET Certification Consultation Position Paper

NASA Technical Reports Server (NTRS)

Null, Cynthia H.

2009-01-01

In June 2004, the June Space Flight Leadership Council (SFLC) assigned an action to the NASA Engineering and Safety Center (NESC) and External Tank (ET) project jointly to characterize the available dataset [of defect sizes from dissections of foam], identify resultant limitations to statistical treatment of ET as-built foam as part of the overall thermal protection system (TPS) certification, and report to the Program Requirements Change Board (PRCB) and SFLC in September 2004. The NESC statistics team was formed to assist the ET statistics group in August 2004. The NESC's conclusions are presented in this report.

Short-term electric power demand forecasting based on economic-electricity transmission model

NASA Astrophysics Data System (ADS)

Li, Wenfeng; Bai, Hongkun; Liu, Wei; Liu, Yongmin; Wang, Yubin Mao; Wang, Jiangbo; He, Dandan

2018-04-01

Short-term electricity demand forecasting is the basic work to ensure safe operation of the power system. In this paper, a practical economic electricity transmission model (EETM) is built. With the intelligent adaptive modeling capabilities of Prognoz Platform 7.2, the econometric model consists of three industrial added value and income levels is firstly built, the electricity demand transmission model is also built. By multiple regression, moving averages and seasonal decomposition, the problem of multiple correlations between variables is effectively overcome in EETM. The validity of EETM is proved by comparison with the actual value of Henan Province. Finally, EETM model is used to forecast the electricity consumption of the 1-4 quarter of 2018.

Statistical Techniques For Real-time Anomaly Detection Using Spark Over Multi-source VMware Performance Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Solaimani, Mohiuddin; Iftekhar, Mohammed; Khan, Latifur

Anomaly detection refers to the identi cation of an irregular or unusual pat- tern which deviates from what is standard, normal, or expected. Such deviated patterns typically correspond to samples of interest and are assigned different labels in different domains, such as outliers, anomalies, exceptions, or malware. Detecting anomalies in fast, voluminous streams of data is a formidable chal- lenge. This paper presents a novel, generic, real-time distributed anomaly detection framework for heterogeneous streaming data where anomalies appear as a group. We have developed a distributed statistical approach to build a model and later use it to detect anomaly. Asmore » a case study, we investigate group anomaly de- tection for a VMware-based cloud data center, which maintains a large number of virtual machines (VMs). We have built our framework using Apache Spark to get higher throughput and lower data processing time on streaming data. We have developed a window-based statistical anomaly detection technique to detect anomalies that appear sporadically. We then relaxed this constraint with higher accuracy by implementing a cluster-based technique to detect sporadic and continuous anomalies. We conclude that our cluster-based technique out- performs other statistical techniques with higher accuracy and lower processing time.« less

Models of grades and tonnages of some lode tin deposits

USGS Publications Warehouse

Menzie, W.D.; Reed, B.L.; Singer, Donald A.

1988-01-01

Descriptive and grade/tonnage models have recently been built for many types of deposits. Such models consist of descriptions of mineralogy, host rocks, ore textures, controls, alteration, geochemical signatures, age, and tectonic settings, together with statistical models of grades, tonnages, and contained metal of deposits of each type. The models are used to identify areas that may contain undiscovered deposits of given types, to convey to non-geologists an idea of the importance of such deposits, and to test and refine classifications of mineral deposits.Descriptive and grade/tonnage models have recently been built for five types of primary tin deposits: rhyolite-hosted such as in Mexico; hydrothermal lodes such as in Cornwall, England, and the Herberton district, Queensland; replacement (or exhalative?) such as Renison Bell, Tasmania; skarn such as at Lost River, Alaska; and greisen such as in the Erzgebirge. Analyses of frequency distributions of tonnage, contained metal, tin grades and the relationships between these variables show that the deposits fall into four well-defined domains that have definite geological characteristics. Rhyolite-hosted, or Mexican, deposits contain a median of 4 t of tin and have a median grade of 0.4% Sn. Hydrothermal lode deposits have the highest grades. Half of such deposits have grades over 1.0% Sn, and the majority contain more than 1,000 t Sn. Large hydrothermal vein deposits contain more than 50,000 t Sn. Replacement (or exhalative?) deposits contain the largest amount of tin (median = 40,000 t). They are only of slightly lower grade (median = 0.80% Sn) than the hydrothermal lodes. Greisen or stockwork deposits have larger tonnages than replacement deposits, but contain less tin (median = 25,000 t).They are also of much lower grade (median = 0.3% Sn). Though grades and tonnages are available for only four skarn deposits, they appear to be more like greisen deposits than replacement deposits when compared using grades, tonnage and contained tin.Although these individual models of primary tin deposits must be regarded as preliminary because of the relatively small number of deposits upon which they are built, they clearly demonstrate differences among types and provide basic information that can be useful in making decisions about exploration strategy, land classification, and tin supply.

Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity.

PubMed

Ahlberg, Ernst; Amberg, Alexander; Beilke, Lisa D; Bower, David; Cross, Kevin P; Custer, Laura; Ford, Kevin A; Van Gompel, Jacky; Harvey, James; Honma, Masamitsu; Jolly, Robert; Joossens, Elisabeth; Kemper, Raymond A; Kenyon, Michelle; Kruhlak, Naomi; Kuhnke, Lara; Leavitt, Penny; Naven, Russell; Neilan, Claire; Quigley, Donald P; Shuey, Dana; Spirkl, Hans-Peter; Stavitskaya, Lidiya; Teasdale, Andrew; White, Angela; Wichard, Joerg; Zwickl, Craig; Myatt, Glenn J

2016-06-01

Statistical-based and expert rule-based models built using public domain mutagenicity knowledge and data are routinely used for computational (Q)SAR assessments of pharmaceutical impurities in line with the approach recommended in the ICH M7 guideline. Knowledge from proprietary corporate mutagenicity databases could be used to increase the predictive performance for selected chemical classes as well as expand the applicability domain of these (Q)SAR models. This paper outlines a mechanism for sharing knowledge without the release of proprietary data. Primary aromatic amine mutagenicity was selected as a case study because this chemical class is often encountered in pharmaceutical impurity analysis and mutagenicity of aromatic amines is currently difficult to predict. As part of this analysis, a series of aromatic amine substructures were defined and the number of mutagenic and non-mutagenic examples for each chemical substructure calculated across a series of public and proprietary mutagenicity databases. This information was pooled across all sources to identify structural classes that activate or deactivate aromatic amine mutagenicity. This structure activity knowledge, in combination with newly released primary aromatic amine data, was incorporated into Leadscope's expert rule-based and statistical-based (Q)SAR models where increased predictive performance was demonstrated. Copyright © 2016 Elsevier Inc. All rights reserved.

Glyphosate detection with ammonium nitrate and humic acids as potential interfering substances by pulsed voltammetry technique.

PubMed

Martínez Gil, Pablo; Laguarda-Miro, Nicolas; Camino, Juan Soto; Peris, Rafael Masot

2013-10-15

Pulsed voltammetry has been used to detect and quantify glyphosate on buffered water in presence of ammonium nitrate and humic substances. Glyphosate is the most widely used herbicide active ingredient in the world. It is a non-selective broad spectrum herbicide but some of its health and environmental effects are still being discussed. Nowadays, glyphosate pollution in water is being monitored but quantification techniques are slow and expensive. Glyphosate wastes are often detected in countryside water bodies where organic substances and fertilizers (commonly based on ammonium nitrate) may also be present. Glyphosate also forms complexes with humic acids so these compounds have also been taken into consideration. The objective of this research is to study the interference of these common pollutants in glyphosate measurements by pulsed voltammetry. The statistical treatment of the voltammetric data obtained lets us discriminate glyphosate from the other studied compounds and a mathematical model has been built to quantify glyphosate concentrations in a buffer despite the presence of humic substances and ammonium nitrate. In this model, the coefficient of determination (R(2)) is 0.977 and the RMSEP value is 2.96 × 10(-5) so the model is considered statistically valid. Copyright © 2013 Elsevier B.V. All rights reserved.

Statistics of Shared Components in Complex Component Systems

NASA Astrophysics Data System (ADS)

Mazzolini, Andrea; Gherardi, Marco; Caselle, Michele; Cosentino Lagomarsino, Marco; Osella, Matteo

2018-04-01

Many complex systems are modular. Such systems can be represented as "component systems," i.e., sets of elementary components, such as LEGO bricks in LEGO sets. The bricks found in a LEGO set reflect a target architecture, which can be built following a set-specific list of instructions. In other component systems, instead, the underlying functional design and constraints are not obvious a priori, and their detection is often a challenge of both scientific and practical importance, requiring a clear understanding of component statistics. Importantly, some quantitative invariants appear to be common to many component systems, most notably a common broad distribution of component abundances, which often resembles the well-known Zipf's law. Such "laws" affect in a general and nontrivial way the component statistics, potentially hindering the identification of system-specific functional constraints or generative processes. Here, we specifically focus on the statistics of shared components, i.e., the distribution of the number of components shared by different system realizations, such as the common bricks found in different LEGO sets. To account for the effects of component heterogeneity, we consider a simple null model, which builds system realizations by random draws from a universe of possible components. Under general assumptions on abundance heterogeneity, we provide analytical estimates of component occurrence, which quantify exhaustively the statistics of shared components. Surprisingly, this simple null model can positively explain important features of empirical component-occurrence distributions obtained from large-scale data on bacterial genomes, LEGO sets, and book chapters. Specific architectural features and functional constraints can be detected from occurrence patterns as deviations from these null predictions, as we show for the illustrative case of the "core" genome in bacteria.

An Evidence-Based Medicine Curriculum Improves General Surgery Residents' Standardized Test Scores in Research and Statistics.

PubMed

Trickey, Amber W; Crosby, Moira E; Singh, Monika; Dort, Jonathan M

2014-12-01

The application of evidence-based medicine to patient care requires unique skills of the physician. Advancing residents' abilities to accurately evaluate the quality of evidence is built on understanding of fundamental research concepts. The American Board of Surgery In-Training Examination (ABSITE) provides a relevant measure of surgical residents' knowledge of research design and statistics. We implemented a research education curriculum in an independent academic medical center general residency program, and assessed the effect on ABSITE scores. The curriculum consisted of five 1-hour monthly research and statistics lectures. The lectures were presented before the 2012 and 2013 examinations. Forty residents completing ABSITE examinations from 2007 to 2013 were included in the study. Two investigators independently identified research-related item topics from examination summary reports. Correct and incorrect responses were compared precurriculum and postcurriculum. Regression models were calculated to estimate improvement in postcurriculum scores, adjusted for individuals' scores over time and postgraduate year level. Residents demonstrated significant improvement in postcurriculum examination scores for research and statistics items. Correct responses increased 27% (P < .001). Residents were 5 times more likely to achieve a perfect score on research and statistics items postcurriculum (P < .001). Residents at all levels demonstrated improved research and statistics scores after receiving the curriculum. Because the ABSITE includes a wide spectrum of research topics, sustained improvements suggest a genuine level of understanding that will promote lifelong evaluation and clinical application of the surgical literature.

The ambiguity of simplicity in quantum and classical simulation

NASA Astrophysics Data System (ADS)

Aghamohammadi, Cina; Mahoney, John R.; Crutchfield, James P.

2017-04-01

A system's perceived simplicity depends on whether it is represented classically or quantally. This is not so surprising, as classical and quantum physics are descriptive frameworks built on different assumptions that capture, emphasize, and express different properties and mechanisms. What is surprising is that, as we demonstrate, simplicity is ambiguous: the relative simplicity between two systems can change sign when moving between classical and quantum descriptions. Here, we associate simplicity with small model-memory. We see that the notions of absolute physical simplicity at best form a partial, not a total, order. This suggests that appeals to principles of physical simplicity, via Ockham's Razor or to the ;elegance; of competing theories, may be fundamentally subjective. Recent rapid progress in quantum computation and quantum simulation suggest that the ambiguity of simplicity will strongly impact statistical inference and, in particular, model selection.

Tensile Properties Characterization of AlSi10Mg Parts Produced by Direct Metal Laser Sintering via Nested Effects Modeling.

PubMed

Palumbo, Biagio; Del Re, Francesco; Martorelli, Massimo; Lanzotti, Antonio; Corrado, Pasquale

2017-02-08

A statistical approach for the characterization of Additive Manufacturing (AM) processes is presented in this paper. Design of Experiments (DOE) and ANalysis of VAriance (ANOVA), both based on Nested Effects Modeling (NEM) technique, are adopted to assess the effect of different laser exposure strategies on physical and mechanical properties of AlSi10Mg parts produced by Direct Metal Laser Sintering (DMLS). Due to the wide industrial interest in AM technologies in many different fields, it is extremely important to ensure high parts performances and productivity. For this aim, the present paper focuses on the evaluation of tensile properties of specimens built with different laser exposure strategies. Two optimal laser parameters settings, in terms of both process quality (part performances) and productivity (part build rate), are identified.

Tensile Properties Characterization of AlSi10Mg Parts Produced by Direct Metal Laser Sintering via Nested Effects Modeling

PubMed Central

Palumbo, Biagio; Del Re, Francesco; Martorelli, Massimo; Lanzotti, Antonio; Corrado, Pasquale

2017-01-01

A statistical approach for the characterization of Additive Manufacturing (AM) processes is presented in this paper. Design of Experiments (DOE) and ANalysis of VAriance (ANOVA), both based on Nested Effects Modeling (NEM) technique, are adopted to assess the effect of different laser exposure strategies on physical and mechanical properties of AlSi10Mg parts produced by Direct Metal Laser Sintering (DMLS). Due to the wide industrial interest in AM technologies in many different fields, it is extremely important to ensure high parts performances and productivity. For this aim, the present paper focuses on the evaluation of tensile properties of specimens built with different laser exposure strategies. Two optimal laser parameters settings, in terms of both process quality (part performances) and productivity (part build rate), are identified. PMID:28772505

Application of factor analysis of infrared spectra for quantitative determination of beta-tricalcium phosphate in calcium hydroxylapatite.

PubMed

Arsenyev, P A; Trezvov, V V; Saratovskaya, N V

1997-01-01

This work represents a method, which allows to determine phase composition of calcium hydroxylapatite basing on its infrared spectrum. The method uses factor analysis of the spectral data of calibration set of samples to determine minimal number of factors required to reproduce the spectra within experimental error. Multiple linear regression is applied to establish correlation between factor scores of calibration standards and their properties. The regression equations can be used to predict the property value of unknown sample. The regression model was built for determination of beta-tricalcium phosphate content in hydroxylapatite. Statistical estimation of quality of the model was carried out. Application of the factor analysis on spectral data allows to increase accuracy of beta-tricalcium phosphate determination and expand the range of determination towards its less concentration. Reproducibility of results is retained.

MHSS: a material handling system simulator

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pomernacki, L.; Hollstien, R.B.

1976-04-07

A Material Handling System Simulator (MHSS) program is described that provides specialized functional blocks for modeling and simulation of nuclear material handling systems. Models of nuclear fuel fabrication plants may be built using functional blocks that simulate material receiving, storage, transport, inventory, processing, and shipping operations as well as the control and reporting tasks of operators or on-line computers. Blocks are also provided that allow the user to observe and gather statistical information on the dynamic behavior of simulated plants over single or replicated runs. Although it is currently being developed for the nuclear materials handling application, MHSS can bemore » adapted to other industries in which material accountability is important. In this paper, emphasis is on the simulation methodology of the MHSS program with application to the nuclear material safeguards problem. (auth)« less

Statistical analysis of native contact formation in the folding of designed model proteins

NASA Astrophysics Data System (ADS)

Tiana, Guido; Broglia, Ricardo A.

2001-02-01

The time evolution of the formation probability of native bonds has been studied for designed sequences which fold fast into the native conformation. From this analysis a clear hierarchy of bonds emerge: (a) local, fast forming highly stable native bonds built by some of the most strongly interacting amino acids of the protein; (b) nonlocal bonds formed late in the folding process, in coincidence with the folding nucleus, and involving essentially the same strongly interacting amino acids already participating in the fast bonds; (c) the rest of the native bonds whose behavior is subordinated, to a large extent, to that of the strong local and nonlocal native contacts.

Role of adjacency-matrix degeneracy in maximum-entropy-weighted network models

NASA Astrophysics Data System (ADS)

Sagarra, O.; Pérez Vicente, C. J.; Díaz-Guilera, A.

2015-11-01

Complex network null models based on entropy maximization are becoming a powerful tool to characterize and analyze data from real systems. However, it is not easy to extract good and unbiased information from these models: A proper understanding of the nature of the underlying events represented in them is crucial. In this paper we emphasize this fact stressing how an accurate counting of configurations compatible with given constraints is fundamental to build good null models for the case of networks with integer-valued adjacency matrices constructed from an aggregation of one or multiple layers. We show how different assumptions about the elements from which the networks are built give rise to distinctively different statistics, even when considering the same observables to match those of real data. We illustrate our findings by applying the formalism to three data sets using an open-source software package accompanying the present work and demonstrate how such differences are clearly seen when measuring network observables.

Software-defined Quantum Networking Ecosystem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Humble, Travis S.; Sadlier, Ronald

The software enables a user to perform modeling and simulation of software-defined quantum networks. The software addresses the problem of how to synchronize transmission of quantum and classical signals through multi-node networks and to demonstrate quantum information protocols such as quantum teleportation. The software approaches this problem by generating a graphical model of the underlying network and attributing properties to each node and link in the graph. The graphical model is then simulated using a combination of discrete-event simulators to calculate the expected state of each node and link in the graph at a future time. A user interacts withmore » the software by providing an initial network model and instantiating methods for the nodes to transmit information with each other. This includes writing application scripts in python that make use of the software library interfaces. A user then initiates the application scripts, which invokes the software simulation. The user then uses the built-in diagnostic tools to query the state of the simulation and to collect statistics on synchronization.« less

South Atlantic anomaly and CubeSat design considerations

NASA Astrophysics Data System (ADS)

Fennelly, Judy A.; Johnston, William R.; Ober, Daniel M.; Wilson, Gordon R.; O'Brien, T. Paul; Huston, Stuart L.

2015-09-01

Effects of the South Atlantic Anomaly (SAA) on spacecraft in low Earth orbit (LEO) are well known and documented. The SAA exposes spacecraft in LEO to high dose of ionizing radiation as well as higher than normal rates of Single Event Upsets (SEU) and Single Event Latch-ups (SEL). CubeSats, spacecraft built around 10 x 10 x 10 cm cubes, are even more susceptible to SEUs and SELs due to the use of commercial off-the-shelf components for electronics and payload instrumentation. Examination of the SAA using both data from the Defense Meteorological Satellite Program (DMSP) and a new set of models for the flux of particles is presented. The models, AE9, AP9, and SPM for energetic electrons, energetic protons and space plasma, were developed for use in space system design. These models introduce databased statistical constraints on the uncertainties from measurements and climatological variability. Discussion of the models' capabilities and limitations with regard to LEO CubeSat design is presented.

Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation

PubMed Central

Kong, Zehui; Liu, Teng

2017-01-01

To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control. PMID:28671967

Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation.

PubMed

Kong, Zehui; Zou, Yuan; Liu, Teng

2017-01-01

To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control.

Error model of geomagnetic-field measurement and extended Kalman-filter based compensation method

PubMed Central

Ge, Zhilei; Liu, Suyun; Li, Guopeng; Huang, Yan; Wang, Yanni

2017-01-01

The real-time accurate measurement of the geomagnetic-field is the foundation to achieving high-precision geomagnetic navigation. The existing geomagnetic-field measurement models are essentially simplified models that cannot accurately describe the sources of measurement error. This paper, on the basis of systematically analyzing the source of geomagnetic-field measurement error, built a complete measurement model, into which the previously unconsidered geomagnetic daily variation field was introduced. This paper proposed an extended Kalman-filter based compensation method, which allows a large amount of measurement data to be used in estimating parameters to obtain the optimal solution in the sense of statistics. The experiment results showed that the compensated strength of the geomagnetic field remained close to the real value and the measurement error was basically controlled within 5nT. In addition, this compensation method has strong applicability due to its easy data collection and ability to remove the dependence on a high-precision measurement instrument. PMID:28445508

Prediction of silicon oxynitride plasma etching using a generalized regression neural network

NASA Astrophysics Data System (ADS)

Kim, Byungwhan; Lee, Byung Teak

2005-08-01

A prediction model of silicon oxynitride (SiON) etching was constructed using a neural network. Model prediction performance was improved by means of genetic algorithm. The etching was conducted in a C2F6 inductively coupled plasma. A 24 full factorial experiment was employed to systematically characterize parameter effects on SiON etching. The process parameters include radio frequency source power, bias power, pressure, and C2F6 flow rate. To test the appropriateness of the trained model, additional 16 experiments were conducted. For comparison, four types of statistical regression models were built. Compared to the best regression model, the optimized neural network model demonstrated an improvement of about 52%. The optimized model was used to infer etch mechanisms as a function of parameters. The pressure effect was noticeably large only as relatively large ion bombardment was maintained in the process chamber. Ion-bombardment-activated polymer deposition played the most significant role in interpreting the complex effect of bias power or C2F6 flow rate. Moreover, [CF2] was expected to be the predominant precursor to polymer deposition.

Evaluation of the built environment: staff and family satisfaction pre- and post-occupancy of the Children's Hospital.

PubMed

Kotzer, Anne Marie; Zacharakis, Susan Koch; Raynolds, Mary; Buenning, Fred

2011-01-01

To evaluate and compare the impact of an existing and newly built hospital environment on family and staff satisfaction related to light, noise, temperature, aesthetics, and amenities, as well as safety, security, and privacy. The United States is engaged in an unprecedented healthcare building boom driven by the need to replace aging facilities, understand the impact of the built environment on quality and safety, incorporate rapidly emerging technologies, and enhance patient- and family-centered care. More importantly, there is heightened attention to creating optimal physical environments to achieve the best possible outcomes for patients, families, and staff. Using a pre-post descriptive survey design, all nursing, social work, therapy staff, and families on selected inpatient units were invited to participate. A demographic form and Family and Staff Satisfaction Surveys were developed and administered pre- and post-occupancy of the new facility. Pre/post mean scores for staff satisfaction improved on all survey subscales with statistically significant improvement (p < .05) in most areas. The most improvement was seen with layout of the patient room, natural light, storage and writing surfaces, and comfort and appeal. Family satisfaction demonstrated statistically significant improvement on all subscales (p ≤ .01), especially for natural light, quiet space, parking, and the child's room as a healing environment. Families and staff reported greater satisfaction with the newly built hospital environment compared to the old facility. Study results will help guide future architectural design decisions, attract and retain staff at a world-class facility, and create the most effective healing environments.

Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC.

PubMed

Contreras-Torres, Ernesto

2018-06-02

In this study, I introduce novel global and local 0D-protein descriptors based on a statistical quantity named Total Sum of Squares (TSS). This quantity represents the sum of the squares differences of amino acid properties from the arithmetic mean property. As an extension, the amino acid-types and amino acid-groups formalisms are used for describing zones of interest in proteins. To assess the effectiveness of the proposed descriptors, a Nearest Neighbor model for predicting the major four protein structural classes was built. This model has a success rate of 98.53% on the jackknife cross-validation test; this performance being superior to other reported methods despite the simplicity of the predictor. Additionally, this predictor has an average success rate of 98.35% in different cross-validation tests performed. A value of 0.98 for the Kappa statistic clearly discriminates this model from a random predictor. The results obtained by the Nearest Neighbor model demonstrated the ability of the proposed descriptors not only to reflect relevant biochemical information related to the structural classes of proteins but also to allow appropriate interpretability. It can thus be expected that the current method may play a supplementary role to other existing approaches for protein structural class prediction and other protein attributes. Copyright © 2018 Elsevier Ltd. All rights reserved.

Understanding the Relationship between Activity and Neighbourhoods (URBAN) Study: research design and methodology.

PubMed

Badland, Hannah M; Schofield, Grant M; Witten, Karen; Schluter, Philip J; Mavoa, Suzanne; Kearns, Robin A; Hinckson, Erica A; Oliver, Melody; Kaiwai, Hector; Jensen, Victoria G; Ergler, Christina; McGrath, Leslie; McPhee, Julia

2009-07-10

Built environment attributes are recognized as being important contributors to physical activity (PA) engagement and body size in adults and children. However, much of the existing research in this emergent public health field is hindered by methodological limitations, including: population and site homogeneity, reliance on self-report measures, aggregated measures of PA, and inadequate statistical modeling. As an integral component of multi-country collaborative research, the Understanding the Relationship between Activity and Neighbourhoods (URBAN) Study seeks to overcome these limitations by determining the strengths of association between detailed measures of the neighborhood built environment with PA levels across multiple domains and body size measures in adults and children. This article outlines the research protocol developed for the URBAN Study. The URBAN Study is a multi-centered, stratified, cross-sectional research design, collecting data across four New Zealand cities. Within each city, 12 neighborhoods were identified and selected for investigation based on higher or lower walkability and Māori demographic attributes. Neighborhoods were selected to ensure equal representation of these characteristics. Within each selected neighborhood, 42 households are being randomly selected and an adult and child (where possible) recruited into the study. Data collection includes: objective and self-reported PA engagement, neighborhood perceptions, demographics, and body size measures. The study was designed to recruit approximately 2,000 adults and 250 children into the project. Other aspects of the study include photovoice, which is a qualitative assessment of built environment features associated with PA engagement, an audit of the neighborhood streetscape environment, and an individualized neighborhood walkability profile centered on each participant's residential address. Multilevel modeling will be used to examine the individual-level and neighborhood-level relationships with PA engagement and body size. The URBAN Study is applying a novel scientifically robust research design to provide urgently needed epidemiological information regarding the associations between the built environment and health outcomes. The findings will contribute to a larger, international initiative in which similar neighborhood selection and PA measurement procedures are utilized across eight countries. Accordingly, this study directly addresses the international priority issues of increasing PA engagement and decreasing obesity levels.

Understanding the Relationship between Activity and Neighbourhoods (URBAN) Study: research design and methodology

PubMed Central

Badland, Hannah M; Schofield, Grant M; Witten, Karen; Schluter, Philip J; Mavoa, Suzanne; Kearns, Robin A; Hinckson, Erica A; Oliver, Melody; Kaiwai, Hector; Jensen, Victoria G; Ergler, Christina; McGrath, Leslie; McPhee, Julia

2009-01-01

Background Built environment attributes are recognized as being important contributors to physical activity (PA) engagement and body size in adults and children. However, much of the existing research in this emergent public health field is hindered by methodological limitations, including: population and site homogeneity, reliance on self-report measures, aggregated measures of PA, and inadequate statistical modeling. As an integral component of multi-country collaborative research, the Understanding the Relationship between Activity and Neighbourhoods (URBAN) Study seeks to overcome these limitations by determining the strengths of association between detailed measures of the neighborhood built environment with PA levels across multiple domains and body size measures in adults and children. This article outlines the research protocol developed for the URBAN Study. Methods and design The URBAN Study is a multi-centered, stratified, cross-sectional research design, collecting data across four New Zealand cities. Within each city, 12 neighborhoods were identified and selected for investigation based on higher or lower walkability and Māori demographic attributes. Neighborhoods were selected to ensure equal representation of these characteristics. Within each selected neighborhood, 42 households are being randomly selected and an adult and child (where possible) recruited into the study. Data collection includes: objective and self-reported PA engagement, neighborhood perceptions, demographics, and body size measures. The study was designed to recruit approximately 2,000 adults and 250 children into the project. Other aspects of the study include photovoice, which is a qualitative assessment of built environment features associated with PA engagement, an audit of the neighborhood streetscape environment, and an individualized neighborhood walkability profile centered on each participant's residential address. Multilevel modeling will be used to examine the individual-level and neighborhood-level relationships with PA engagement and body size. Discussion The URBAN Study is applying a novel scientifically robust research design to provide urgently needed epidemiological information regarding the associations between the built environment and health outcomes. The findings will contribute to a larger, international initiative in which similar neighborhood selection and PA measurement procedures are utilized across eight countries. Accordingly, this study directly addresses the international priority issues of increasing PA engagement and decreasing obesity levels. PMID:19589175

Prostate segmentation in MR images using discriminant boundary features.

PubMed

Yang, Meijuan; Li, Xuelong; Turkbey, Baris; Choyke, Peter L; Yan, Pingkun

2013-02-01

Segmentation of the prostate in magnetic resonance image has become more in need for its assistance to diagnosis and surgical planning of prostate carcinoma. Due to the natural variability of anatomical structures, statistical shape model has been widely applied in medical image segmentation. Robust and distinctive local features are critical for statistical shape model to achieve accurate segmentation results. The scale invariant feature transformation (SIFT) has been employed to capture the information of the local patch surrounding the boundary. However, when SIFT feature being used for segmentation, the scale and variance are not specified with the location of the point of interest. To deal with it, the discriminant analysis in machine learning is introduced to measure the distinctiveness of the learned SIFT features for each landmark directly and to make the scale and variance adaptive to the locations. As the gray values and gradients vary significantly over the boundary of the prostate, separate appearance descriptors are built for each landmark and then optimized. After that, a two stage coarse-to-fine segmentation approach is carried out by incorporating the local shape variations. Finally, the experiments on prostate segmentation from MR image are conducted to verify the efficiency of the proposed algorithms.

Mapping of epistatic quantitative trait loci in four-way crosses.

PubMed

He, Xiao-Hong; Qin, Hongde; Hu, Zhongli; Zhang, Tianzhen; Zhang, Yuan-Ming

2011-01-01

Four-way crosses (4WC) involving four different inbred lines often appear in plant and animal commercial breeding programs. Direct mapping of quantitative trait loci (QTL) in these commercial populations is both economical and practical. However, the existing statistical methods for mapping QTL in a 4WC population are built on the single-QTL genetic model. This simple genetic model fails to take into account QTL interactions, which play an important role in the genetic architecture of complex traits. In this paper, therefore, we attempted to develop a statistical method to detect epistatic QTL in 4WC population. Conditional probabilities of QTL genotypes, computed by the multi-point single locus method, were used to sample the genotypes of all putative QTL in the entire genome. The sampled genotypes were used to construct the design matrix for QTL effects. All QTL effects, including main and epistatic effects, were simultaneously estimated by the penalized maximum likelihood method. The proposed method was confirmed by a series of Monte Carlo simulation studies and real data analysis of cotton. The new method will provide novel tools for the genetic dissection of complex traits, construction of QTL networks, and analysis of heterosis.

Middle Rio Grande Cooperative Water Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tidwell, Vince; Passell, Howard

2005-11-01

This is computer simulation model built in a commercial modeling product Called Studio Expert, developed by Powersim, Inc. The simulation model is built in a system dynamics environment, allowing the simulation of the interaction among multiple systems that are all changing over time. The model focuses on hydrology, ecology, demography, and economy of the Middle Rio Grande, with Water as the unifying feature.

Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Data Sets.

PubMed

Marchese Robinson, Richard L; Palczewska, Anna; Palczewski, Jan; Kidley, Nathan

2017-08-28

The ability to interpret the predictions made by quantitative structure-activity relationships (QSARs) offers a number of advantages. While QSARs built using nonlinear modeling approaches, such as the popular Random Forest algorithm, might sometimes be more predictive than those built using linear modeling approaches, their predictions have been perceived as difficult to interpret. However, a growing number of approaches have been proposed for interpreting nonlinear QSAR models in general and Random Forest in particular. In the current work, we compare the performance of Random Forest to those of two widely used linear modeling approaches: linear Support Vector Machines (SVMs) (or Support Vector Regression (SVR)) and partial least-squares (PLS). We compare their performance in terms of their predictivity as well as the chemical interpretability of the predictions using novel scoring schemes for assessing heat map images of substructural contributions. We critically assess different approaches for interpreting Random Forest models as well as for obtaining predictions from the forest. We assess the models on a large number of widely employed public-domain benchmark data sets corresponding to regression and binary classification problems of relevance to hit identification and toxicology. We conclude that Random Forest typically yields comparable or possibly better predictive performance than the linear modeling approaches and that its predictions may also be interpreted in a chemically and biologically meaningful way. In contrast to earlier work looking at interpretation of nonlinear QSAR models, we directly compare two methodologically distinct approaches for interpreting Random Forest models. The approaches for interpreting Random Forest assessed in our article were implemented using open-source programs that we have made available to the community. These programs are the rfFC package ( https://r-forge.r-project.org/R/?group_id=1725 ) for the R statistical programming language and the Python program HeatMapWrapper [ https://doi.org/10.5281/zenodo.495163 ] for heat map generation.

Epidemiologic programs for computers and calculators. A microcomputer program for multiple logistic regression by unconditional and conditional maximum likelihood methods.

PubMed

Campos-Filho, N; Franco, E L

1989-02-01

A frequent procedure in matched case-control studies is to report results from the multivariate unmatched analyses if they do not differ substantially from the ones obtained after conditioning on the matching variables. Although conceptually simple, this rule requires that an extensive series of logistic regression models be evaluated by both the conditional and unconditional maximum likelihood methods. Most computer programs for logistic regression employ only one maximum likelihood method, which requires that the analyses be performed in separate steps. This paper describes a Pascal microcomputer (IBM PC) program that performs multiple logistic regression by both maximum likelihood estimation methods, which obviates the need for switching between programs to obtain relative risk estimates from both matched and unmatched analyses. The program calculates most standard statistics and allows factoring of categorical or continuous variables by two distinct methods of contrast. A built-in, descriptive statistics option allows the user to inspect the distribution of cases and controls across categories of any given variable.

Optical mass memory investigation

NASA Technical Reports Server (NTRS)

1980-01-01

The MASTER 1 optical mass storage system advanced working model (AWM) was designed to demonstrate recording and playback of imagery data and to enable quantitative data to be derived as to the statistical distribution of raw errors experienced through the system. The AWM consists of two subsystems, the recorder and storage and retrieval. The recorder subsystem utilizes key technologies such as an acoustic travelling wave lens to achieve recording of digital data on fiche at a rate of 30 Mbits/sec, whereas the storage and retrieval reproducer subsystem utilizes a less complex optical system that employs an acousto-optical beam deflector to achieve data readout at a 5 Mbits/sec rate. The system has the built in capability for detecting and collecting error statistics. The recorder and storage and retrieval subsystems operate independent of one another and are each constructed in modular form with each module performing independent functions. The operation of each module and its interface to other modules is controlled by one controller for both subsystems.

Hunting high and low: disentangling primordial and late-time non-Gaussianity with cosmic densities in spheres

NASA Astrophysics Data System (ADS)

Uhlemann, C.; Pajer, E.; Pichon, C.; Nishimichi, T.; Codis, S.; Bernardeau, F.

2018-03-01

Non-Gaussianities of dynamical origin are disentangled from primordial ones using the formalism of large deviation statistics with spherical collapse dynamics. This is achieved by relying on accurate analytical predictions for the one-point probability distribution function and the two-point clustering of spherically averaged cosmic densities (sphere bias). Sphere bias extends the idea of halo bias to intermediate density environments and voids as underdense regions. In the presence of primordial non-Gaussianity, sphere bias displays a strong scale dependence relevant for both high- and low-density regions, which is predicted analytically. The statistics of densities in spheres are built to model primordial non-Gaussianity via an initial skewness with a scale dependence that depends on the bispectrum of the underlying model. The analytical formulas with the measured non-linear dark matter variance as input are successfully tested against numerical simulations. For local non-Gaussianity with a range from fNL = -100 to +100, they are found to agree within 2 per cent or better for densities ρ ∈ [0.5, 3] in spheres of radius 15 Mpc h-1 down to z = 0.35. The validity of the large deviation statistics formalism is thereby established for all observationally relevant local-type departures from perfectly Gaussian initial conditions. The corresponding estimators for the amplitude of the non-linear variance σ8 and primordial skewness fNL are validated using a fiducial joint maximum likelihood experiment. The influence of observational effects and the prospects for a future detection of primordial non-Gaussianity from joint one- and two-point densities-in-spheres statistics are discussed.

[Assessment of land use environmental impacts in urban built-up area: a case study in main built-up area of Nanchang City].

PubMed

Chen, Wen-Bo; Liu, Shi-Yu; Yu, Dun; Zou, Qiu-Ming

2009-07-01

Based on the relevant studies of land use environmental impacts and the characteristics of urban land use, a conceptual model on the assessment of land use environmental impacts in urban built-up area was established. This model grouped the land use environmental impacts in built-up area into four basic processes, i. e., detailization, abstractization, matching, and evaluation. A case study was conducted in the main built-up area of Nanchang City, with noise, smell, dust, and hazard as the impact factors. In the test area, noise had a widespread impact, its impacting area accounting for 59% of the total, smell and dust impacts centralized in the east and south parts, while hazard impact was centralized in the southeast part, an industrial area. This assessment model of four basic processes was practical, and could provide basis for the decision-making of urban land use management and planning.

Estimating state of charge and health of lithium-ion batteries with guided waves using built-in piezoelectric sensors/actuators

NASA Astrophysics Data System (ADS)

Ladpli, Purim; Kopsaftopoulos, Fotis; Chang, Fu-Kuo

2018-04-01

This work presents the feasibility of monitoring state of charge (SoC) and state of health (SoH) of lithium-ion pouch batteries with acousto-ultrasonic guided waves. The guided waves are propagated and sensed using low-profile, built-in piezoelectric disc transducers that can be retrofitted onto off-the-shelf batteries. Both experimental and analytical studies are performed to understand the relationship between guided waves generated in a pitch-catch mode and battery SoC/SoH. The preliminary experiments on representative pouch cells show that the changes in time of flight (ToF) and signal amplitude (SA) resulting from shifts in the guided wave signals correlate strongly with the electrochemical charge-discharge cycling and aging. An analytical acoustic model is developed to simulate the variations in electrode moduli and densities during cycling, which correctly validates the absolute values and range of experimental ToF. It is further illustrated via a statistical study that ToF and SA can be used in a prediction model to accurately estimate SoC/SoH. Additionally, by using multiple sensors in a network configuration on the same battery, a significantly more reliable and accurate SoC/SoH prediction is achieved. The indicative results from this study can be extended to develop a unified guided-wave-based framework for SoC/SoH monitoring of many lithium-ion battery applications.

Application of Statistically Derived CPAS Parachute Parameters

NASA Technical Reports Server (NTRS)

Romero, Leah M.; Ray, Eric S.

2013-01-01

The Capsule Parachute Assembly System (CPAS) Analysis Team is responsible for determining parachute inflation parameters and dispersions that are ultimately used in verifying system requirements. A model memo is internally released semi-annually documenting parachute inflation and other key parameters reconstructed from flight test data. Dispersion probability distributions published in previous versions of the model memo were uniform because insufficient data were available for determination of statistical based distributions. Uniform distributions do not accurately represent the expected distributions since extreme parameter values are just as likely to occur as the nominal value. CPAS has taken incremental steps to move away from uniform distributions. Model Memo version 9 (MMv9) made the first use of non-uniform dispersions, but only for the reefing cutter timing, for which a large number of sample was available. In order to maximize the utility of the available flight test data, clusters of parachutes were reconstructed individually starting with Model Memo version 10. This allowed for statistical assessment for steady-state drag area (CDS) and parachute inflation parameters such as the canopy fill distance (n), profile shape exponent (expopen), over-inflation factor (C(sub k)), and ramp-down time (t(sub k)) distributions. Built-in MATLAB distributions were applied to the histograms, and parameters such as scale (sigma) and location (mu) were output. Engineering judgment was used to determine the "best fit" distribution based on the test data. Results include normal, log normal, and uniform (where available data remains insufficient) fits of nominal and failure (loss of parachute and skipped stage) cases for all CPAS parachutes. This paper discusses the uniform methodology that was previously used, the process and result of the statistical assessment, how the dispersions were incorporated into Monte Carlo analyses, and the application of the distributions in trajectory benchmark testing assessments with parachute inflation parameters, drag area, and reefing cutter timing used by CPAS.

Flexible parametric survival models built on age-specific antimüllerian hormone percentiles are better predictors of menopause.

PubMed

Ramezani Tehrani, Fahimeh; Mansournia, Mohammad Ali; Solaymani-Dodaran, Masoud; Steyerberg, Ewout; Azizi, Fereidoun

2016-06-01

This study aimed to improve existing prediction models for age at menopause. We identified all reproductive aged women with regular menstrual cycles who met our eligibility criteria (n = 1,015) in the Tehran Lipid and Glucose Study-an ongoing population-based cohort study initiated in 1998. Participants were examined every 3 years and their reproductive histories were recorded. Blood levels of antimüllerian hormone (AMH) were measured at the time of recruitment. Age at menopause was estimated based on serum concentrations of AMH using flexible parametric survival models. The optimum model was selected according to Akaike Information Criteria and the realness of the range of predicted median menopause age. We followed study participants for a median of 9.8 years during which 277 women reached menopause and found that a spline-based proportional odds model including age-specific AMH percentiles as the covariate performed well in terms of statistical criteria and provided the most clinically relevant and realistic predictions. The range of predicted median age at menopause for this model was 47.1 to 55.9 years. For those who reached menopause, the median of the absolute mean difference between actual and predicted age at menopause was 1.9 years (interquartile range 2.9). The model including the age-specific AMH percentiles as the covariate and using proportional odds as its covariate metrics meets all the statistical criteria for the best model and provides the most clinically relevant and realistic predictions for age at menopause for reproductive-aged women.

Sentinel node status prediction by four statistical models: results from a large bi-institutional series (n = 1132).

PubMed

Mocellin, Simone; Thompson, John F; Pasquali, Sandro; Montesco, Maria C; Pilati, Pierluigi; Nitti, Donato; Saw, Robyn P; Scolyer, Richard A; Stretch, Jonathan R; Rossi, Carlo R

2009-12-01

To improve selection for sentinel node (SN) biopsy (SNB) in patients with cutaneous melanoma using statistical models predicting SN status. About 80% of patients currently undergoing SNB are node negative. In the absence of conclusive evidence of a SNBassociated survival benefit, these patients may be over-treated. Here, we tested the efficiency of 4 different models in predicting SN status. The clinicopathologic data (age, gender, tumor thickness, Clark level, regression, ulceration, histologic subtype, and mitotic index) of 1132 melanoma patients who had undergone SNB at institutions in Italy and Australia were analyzed. Logistic regression, classification tree, random forest, and support vector machine models were fitted to the data. The predictive models were built with the aim of maximizing the negative predictive value (NPV) and reducing the rate of SNB procedures though minimizing the error rate. After cross-validation logistic regression, classification tree, random forest, and support vector machine predictive models obtained clinically relevant NPV (93.6%, 94.0%, 97.1%, and 93.0%, respectively), SNB reduction (27.5%, 29.8%, 18.2%, and 30.1%, respectively), and error rates (1.8%, 1.8%, 0.5%, and 2.1%, respectively). Using commonly available clinicopathologic variables, predictive models can preoperatively identify a proportion of patients ( approximately 25%) who might be spared SNB, with an acceptable (1%-2%) error. If validated in large prospective series, these models might be implemented in the clinical setting for improved patient selection, which ultimately would lead to better quality of life for patients and optimization of resource allocation for the health care system.

[Comparison between administrative and clinical databases in the evaluation of cardiac surgery performance].

PubMed

Rosato, Stefano; D'Errigo, Paola; Badoni, Gabriella; Fusco, Danilo; Perucci, Carlo A; Seccareccia, Fulvia

2008-08-01

The availability of two contemporary sources of information about coronary artery bypass graft (CABG) interventions, allowed 1) to verify the feasibility of performing outcome evaluation studies using administrative data sources, and 2) to compare hospital performance obtainable using the CABG Project clinical database with hospital performance derived from the use of current administrative data. Interventions recorded in the CABG Project were linked to the hospital discharge record (HDR) administrative database. Only the linked records were considered for subsequent analyses (46% of the total CABG Project). A new selected population "clinical card-HDR" was then defined. Two independent risk-adjustment models were applied, each of them using information derived from one of the two different sources. Then, HDR information was supplemented with some patient preoperative conditions from the CABG clinical database. The two models were compared in terms of their adaptability to data. Hospital performances identified by the two different models and significantly different from the mean was compared. In only 4 of the 13 hospitals considered for analysis, the results obtained using the HDR model did not completely overlap with those obtained by the CABG model. When comparing statistical parameters of the HDR model and the HDR model + patient preoperative conditions, the latter showed the best adaptability to data. In this "clinical card-HDR" population, hospital performance assessment obtained using information from the clinical database is similar to that derived from the use of current administrative data. However, when risk-adjustment models built on administrative databases are supplemented with a few clinical variables, their statistical parameters improve and hospital performance assessment becomes more accurate.

3D QSAR models built on structure-based alignments of Abl tyrosine kinase inhibitors.

PubMed

Falchi, Federico; Manetti, Fabrizio; Carraro, Fabio; Naldini, Antonella; Maga, Giovanni; Crespan, Emmanuele; Schenone, Silvia; Bruno, Olga; Brullo, Chiara; Botta, Maurizio

2009-06-01

Quality QSAR: A combination of docking calculations and a statistical approach toward Abl inhibitors resulted in a 3D QSAR model, the analysis of which led to the identification of ligand portions important for affinity. New compounds designed on the basis of the model were found to have very good affinity for the target, providing further validation of the model itself.The X-ray crystallographic coordinates of the Abl tyrosine kinase domain in its active, inactive, and Src-like inactive conformations were used as targets to simulate the binding mode of a large series of pyrazolo[3,4-d]pyrimidines (known Abl inhibitors) by means of GOLD software. Receptor-based alignments provided by molecular docking calculations were submitted to a GRID-GOLPE protocol to generate 3D QSAR models. Analysis of the results showed that the models based on the inactive and Src-like inactive conformations had very poor statistical parameters, whereas the sole model based on the active conformation of Abl was characterized by significant internal and external predictive ability. Subsequent analysis of GOLPE PLS pseudo-coefficient contour plots of this model gave us a better understanding of the relationships between structure and affinity, providing suggestions for the next optimization process. On the basis of these results, new compounds were designed according to the hydrophobic and hydrogen bond donor and acceptor contours, and were found to have improved enzymatic and cellular activity with respect to parent compounds. Additional biological assays confirmed the important role of the selected compounds as inhibitors of cell proliferation in leukemia cells.

A GIS based model for active transportation in the built environment

NASA Astrophysics Data System (ADS)

Addison, Veronica Marie Medina

Obesity and physical inactivity have been major risk factors associated with morbidity and mortality in the United States. Recently, obesity and physical inactivity have been on the rise. Determining connections between this trend and the environment could lead to a built environment that is conducive to healthy, active people. In my previous research, I have studied the built environment and its connection to health. For my dissertation, I build on this fundamental work by incorporating energy, specifically by studying the built environment and its connection to energy expenditures. This research models the built environment and combines this with human energy expenditure information in order to provide a planning tool that allows an individual to actively address health issues, particularly obesity. This research focuses on the design and development of an internet based model that enables individuals to understand their own energy expenditures in relation to their environment. The model will work to find the energy consumed by an individual in their navigation through campus. This is accomplished by using Geographic Information Systems (GIS) to model the campus and using it as the basis for calculating energy expended through active transportation. Using GIS to create the model allows for the incorporation of built environment factors such as elevation and energy expenditures in relation to physical exertion rate. This research will contribute to the long-term solution to the obesity epidemic by creating healthy communities through smart growth and sustainable design. This research provides users with a tool to use in their current environment for their personal and community well being.

Constant pressure and temperature discrete-time Langevin molecular dynamics

NASA Astrophysics Data System (ADS)

Grønbech-Jensen, Niels; Farago, Oded

2014-11-01

We present a new and improved method for simultaneous control of temperature and pressure in molecular dynamics simulations with periodic boundary conditions. The thermostat-barostat equations are built on our previously developed stochastic thermostat, which has been shown to provide correct statistical configurational sampling for any time step that yields stable trajectories. Here, we extend the method and develop a set of discrete-time equations of motion for both particle dynamics and system volume in order to seek pressure control that is insensitive to the choice of the numerical time step. The resulting method is simple, practical, and efficient. The method is demonstrated through direct numerical simulations of two characteristic model systems—a one-dimensional particle chain for which exact statistical results can be obtained and used as benchmarks, and a three-dimensional system of Lennard-Jones interacting particles simulated in both solid and liquid phases. The results, which are compared against the method of Kolb and Dünweg [J. Chem. Phys. 111, 4453 (1999)], show that the new method behaves according to the objective, namely that acquired statistical averages and fluctuations of configurational measures are accurate and robust against the chosen time step applied to the simulation.

Gis-Based Spatial Statistical Analysis of College Graduates Employment

NASA Astrophysics Data System (ADS)

Tang, R.

2012-07-01

It is urgently necessary to be aware of the distribution and employment status of college graduates for proper allocation of human resources and overall arrangement of strategic industry. This study provides empirical evidence regarding the use of geocoding and spatial analysis in distribution and employment status of college graduates based on the data from 2004-2008 Wuhan Municipal Human Resources and Social Security Bureau, China. Spatio-temporal distribution of employment unit were analyzed with geocoding using ArcGIS software, and the stepwise multiple linear regression method via SPSS software was used to predict the employment and to identify spatially associated enterprise and professionals demand in the future. The results show that the enterprises in Wuhan east lake high and new technology development zone increased dramatically from 2004 to 2008, and tended to distributed southeastward. Furthermore, the models built by statistical analysis suggest that the specialty of graduates major in has an important impact on the number of the employment and the number of graduates engaging in pillar industries. In conclusion, the combination of GIS and statistical analysis which helps to simulate the spatial distribution of the employment status is a potential tool for human resource development research.

Distributed Lag Models: Examining Associations between the Built Environment and Health

PubMed Central

Baek, Jonggyu; Sánchez, Brisa N.; Berrocal, Veronica J.; Sanchez-Vaznaugh, Emma V.

2016-01-01

Built environment factors constrain individual level behaviors and choices, and thus are receiving increasing attention to assess their influence on health. Traditional regression methods have been widely used to examine associations between built environment measures and health outcomes, where a fixed, pre-specified spatial scale (e.g., 1 mile buffer) is used to construct environment measures. However, the spatial scale for these associations remains largely unknown and misspecifying it introduces bias. We propose the use of distributed lag models (DLMs) to describe the association between built environment features and health as a function of distance from the locations of interest and circumvent a-priori selection of a spatial scale. Based on simulation studies, we demonstrate that traditional regression models produce associations biased away from the null when there is spatial correlation among the built environment features. Inference based on DLMs is robust under a range of scenarios of the built environment. We use this innovative application of DLMs to examine the association between the availability of convenience stores near California public schools, which may affect children’s dietary choices both through direct access to junk food and exposure to advertisement, and children’s body mass index z-scores (BMIz). PMID:26414942

VoxelStats: A MATLAB Package for Multi-Modal Voxel-Wise Brain Image Analysis.

PubMed

Mathotaarachchi, Sulantha; Wang, Seqian; Shin, Monica; Pascoal, Tharick A; Benedet, Andrea L; Kang, Min Su; Beaudry, Thomas; Fonov, Vladimir S; Gauthier, Serge; Labbe, Aurélie; Rosa-Neto, Pedro

2016-01-01

In healthy individuals, behavioral outcomes are highly associated with the variability on brain regional structure or neurochemical phenotypes. Similarly, in the context of neurodegenerative conditions, neuroimaging reveals that cognitive decline is linked to the magnitude of atrophy, neurochemical declines, or concentrations of abnormal protein aggregates across brain regions. However, modeling the effects of multiple regional abnormalities as determinants of cognitive decline at the voxel level remains largely unexplored by multimodal imaging research, given the high computational cost of estimating regression models for every single voxel from various imaging modalities. VoxelStats is a voxel-wise computational framework to overcome these computational limitations and to perform statistical operations on multiple scalar variables and imaging modalities at the voxel level. VoxelStats package has been developed in Matlab(®) and supports imaging formats such as Nifti-1, ANALYZE, and MINC v2. Prebuilt functions in VoxelStats enable the user to perform voxel-wise general and generalized linear models and mixed effect models with multiple volumetric covariates. Importantly, VoxelStats can recognize scalar values or image volumes as response variables and can accommodate volumetric statistical covariates as well as their interaction effects with other variables. Furthermore, this package includes built-in functionality to perform voxel-wise receiver operating characteristic analysis and paired and unpaired group contrast analysis. Validation of VoxelStats was conducted by comparing the linear regression functionality with existing toolboxes such as glim_image and RMINC. The validation results were identical to existing methods and the additional functionality was demonstrated by generating feature case assessments (t-statistics, odds ratio, and true positive rate maps). In summary, VoxelStats expands the current methods for multimodal imaging analysis by allowing the estimation of advanced regional association metrics at the voxel level.

Parametric Cost Modeling of Space Missions Using the Develop New Projects (DMP) Implementation Process

NASA Technical Reports Server (NTRS)

Rosenberg, Leigh; Hihn, Jairus; Roust, Kevin; Warfield, Keith

2000-01-01

This paper presents an overview of a parametric cost model that has been built at JPL to estimate costs of future, deep space, robotic science missions. Due to the recent dramatic changes in JPL business practices brought about by an internal reengineering effort known as develop new products (DNP), high-level historic cost data is no longer considered analogous to future missions. Therefore, the historic data is of little value in forecasting costs for projects developed using the DNP process. This has lead to the development of an approach for obtaining expert opinion and also for combining actual data with expert opinion to provide a cost database for future missions. In addition, the DNP cost model has a maximum of objective cost drivers which reduces the likelihood of model input error. Version 2 is now under development which expands the model capabilities, links it more tightly with key design technical parameters, and is grounded in more rigorous statistical techniques. The challenges faced in building this model will be discussed, as well as it's background, development approach, status, validation, and future plans.

Identifying the impact of the built environment on flood damage in Texas.

PubMed

Brody, Samuel D; Zahran, Sammy; Highfield, Wesley E; Grover, Himanshu; Vedlitz, Arnold

2008-03-01

Floods continue to pose the greatest threat to the property and safety of human communities among all natural hazards in the United States. This study examines the relationship between the built environment and flood impacts in Texas, which consistently sustains the most damage from flooding of any other state in the country. Specifically, we calculate property damage resulting from 423 flood events between 1997 and 2001 at the county level. We identify the effect of several built environment measures, including wetland alteration, impervious surface, and dams on reported property damage while controlling for biophysical and socio-economic characteristics. Statistical results suggest that naturally occurring wetlands play a particularly important role in mitigating flood damage. These findings provide guidance to planners and flood managers on how to alleviate most effectively the costly impacts of foods at the community level.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rueegsegger, Michael B.; Bach Cuadra, Meritxell; Pica, Alessia

Purpose: Ocular anatomy and radiation-associated toxicities provide unique challenges for external beam radiation therapy. For treatment planning, precise modeling of organs at risk and tumor volume are crucial. Development of a precise eye model and automatic adaptation of this model to patients' anatomy remain problematic because of organ shape variability. This work introduces the application of a 3-dimensional (3D) statistical shape model as a novel method for precise eye modeling for external beam radiation therapy of intraocular tumors. Methods and Materials: Manual and automatic segmentations were compared for 17 patients, based on head computed tomography (CT) volume scans. A 3Dmore » statistical shape model of the cornea, lens, and sclera as well as of the optic disc position was developed. Furthermore, an active shape model was built to enable automatic fitting of the eye model to CT slice stacks. Cross-validation was performed based on leave-one-out tests for all training shapes by measuring dice coefficients and mean segmentation errors between automatic segmentation and manual segmentation by an expert. Results: Cross-validation revealed a dice similarity of 95% {+-} 2% for the sclera and cornea and 91% {+-} 2% for the lens. Overall, mean segmentation error was found to be 0.3 {+-} 0.1 mm. Average segmentation time was 14 {+-} 2 s on a standard personal computer. Conclusions: Our results show that the solution presented outperforms state-of-the-art methods in terms of accuracy, reliability, and robustness. Moreover, the eye model shape as well as its variability is learned from a training set rather than by making shape assumptions (eg, as with the spherical or elliptical model). Therefore, the model appears to be capable of modeling nonspherically and nonelliptically shaped eyes.« less

Species distribution models: A comparison of statistical approaches for livestock and disease epidemics.

PubMed

Hollings, Tracey; Robinson, Andrew; van Andel, Mary; Jewell, Chris; Burgman, Mark

2017-01-01

In livestock industries, reliable up-to-date spatial distribution and abundance records for animals and farms are critical for governments to manage and respond to risks. Yet few, if any, countries can afford to maintain comprehensive, up-to-date agricultural census data. Statistical modelling can be used as a proxy for such data but comparative modelling studies have rarely been undertaken for livestock populations. Widespread species, including livestock, can be difficult to model effectively due to complex spatial distributions that do not respond predictably to environmental gradients. We assessed three machine learning species distribution models (SDM) for their capacity to estimate national-level farm animal population numbers within property boundaries: boosted regression trees (BRT), random forests (RF) and K-nearest neighbour (K-NN). The models were built from a commercial livestock database and environmental and socio-economic predictor data for New Zealand. We used two spatial data stratifications to test (i) support for decision making in an emergency response situation, and (ii) the ability for the models to predict to new geographic regions. The performance of the three model types varied substantially, but the best performing models showed very high accuracy. BRTs had the best performance overall, but RF performed equally well or better in many simulations; RFs were superior at predicting livestock numbers for all but very large commercial farms. K-NN performed poorly relative to both RF and BRT in all simulations. The predictions of both multi species and single species models for farms and within hypothetical quarantine zones were very close to observed data. These models are generally applicable for livestock estimation with broad applications in disease risk modelling, biosecurity, policy and planning.

Species distribution models: A comparison of statistical approaches for livestock and disease epidemics

PubMed Central

Robinson, Andrew; van Andel, Mary; Jewell, Chris; Burgman, Mark

2017-01-01

In livestock industries, reliable up-to-date spatial distribution and abundance records for animals and farms are critical for governments to manage and respond to risks. Yet few, if any, countries can afford to maintain comprehensive, up-to-date agricultural census data. Statistical modelling can be used as a proxy for such data but comparative modelling studies have rarely been undertaken for livestock populations. Widespread species, including livestock, can be difficult to model effectively due to complex spatial distributions that do not respond predictably to environmental gradients. We assessed three machine learning species distribution models (SDM) for their capacity to estimate national-level farm animal population numbers within property boundaries: boosted regression trees (BRT), random forests (RF) and K-nearest neighbour (K-NN). The models were built from a commercial livestock database and environmental and socio-economic predictor data for New Zealand. We used two spatial data stratifications to test (i) support for decision making in an emergency response situation, and (ii) the ability for the models to predict to new geographic regions. The performance of the three model types varied substantially, but the best performing models showed very high accuracy. BRTs had the best performance overall, but RF performed equally well or better in many simulations; RFs were superior at predicting livestock numbers for all but very large commercial farms. K-NN performed poorly relative to both RF and BRT in all simulations. The predictions of both multi species and single species models for farms and within hypothetical quarantine zones were very close to observed data. These models are generally applicable for livestock estimation with broad applications in disease risk modelling, biosecurity, policy and planning. PMID:28837685

Quantification of uncertainty associated with United States high resolution fossil fuel CO2 emissions: updates, challenges and future plans

NASA Astrophysics Data System (ADS)

Gurney, K. R.; Chandrasekaran, V.; Mendoza, D. L.; Geethakumar, S.

2010-12-01

The Vulcan Project has estimated United States fossil fuel CO2 emissions at the hourly time scale and at spatial scales below the county level for the year 2002. Vulcan is built from a wide variety of observational data streams including regulated air pollutant emissions reporting, traffic monitoring, energy statistics, and US census data. In addition to these data sets, Vulcan relies on a series of modeling assumptions and constructs to interpolate in space, time and transform non-CO2 reporting into an estimate of CO2 combustion emissions. The recent version 2.0 of the Vulcan inventory has produced advances in a number of categories with particular emphasis on improved temporal structure. Onroad transportation emissions now avail of roughly 5000 automated traffic count monitors allowing for much improved diurnal and weekly time structure in our onroad transportation emissions. Though the inventory shows excellent agreement with independent national-level CO2 emissions estimates, uncertainty quantification has been a challenging task given the large number of data sources and numerous modeling assumptions. However, we have now accomplished a complete uncertainty estimate across all the Vulcan economic sectors and will present uncertainty estimates as a function of space, time, sector and fuel. We find that, like the underlying distribution of CO2 emissions themselves, the uncertainty is also strongly lognormal with high uncertainty associated with a relatively small number of locations. These locations typically are locations reliant upon coal combustion as the dominant CO2 source. We will also compare and contrast Vulcan fossil fuel CO2 emissions estimates against estimates built from DOE fuel-based surveys at the state level. We conclude that much of the difference between the Vulcan inventory and DOE statistics are not due to biased estimation but mechanistic differences in supply versus demand and combustion in space/time.

Multilayer perceptron neural network-based approach for modeling phycocyanin pigment concentrations: case study from lower Charles River buoy, USA.

PubMed

Heddam, Salim

2016-09-01

This paper proposes multilayer perceptron neural network (MLPNN) to predict phycocyanin (PC) pigment using water quality variables as predictor. In the proposed model, four water quality variables that are water temperature, dissolved oxygen, pH, and specific conductance were selected as the inputs for the MLPNN model, and the PC as the output. To demonstrate the capability and the usefulness of the MLPNN model, a total of 15,849 data measured at 15-min (15 min) intervals of time are used for the development of the model. The data are collected at the lower Charles River buoy, and available from the US Environmental Protection Agency (USEPA). For comparison purposes, a multiple linear regression (MLR) model that was frequently used for predicting water quality variables in previous studies is also built. The performances of the models are evaluated using a set of widely used statistical indices. The performance of the MLPNN and MLR models is compared with the measured data. The obtained results show that (i) the all proposed MLPNN models are more accurate than the MLR models and (ii) the results obtained are very promising and encouraging for the development of phycocyanin-predictive models.

In search of causality: a systematic review of the relationship between the built environment and physical activity among adults

PubMed Central

2011-01-01

Background Empirical evidence suggests that an association between the built environment and physical activity exists. This evidence is mostly derived from cross-sectional studies that do not account for other causal explanations such as neighborhood self-selection. Experimental and quasi-experimental designs can be used to isolate the effect of the built environment on physical activity, but in their absence, statistical techniques that adjust for neighborhood self-selection can be used with cross-sectional data. Previous reviews examining the built environment-physical activity relationship have not differentiated among findings based on study design. To deal with self-selection, we synthesized evidence regarding the relationship between objective measures of the built environment and physical activity by including in our review: 1) cross-sectional studies that adjust for neighborhood self-selection and 2) quasi-experiments. Method In September 2010, we searched for English-language studies on built environments and physical activity from all available years in health, leisure, transportation, social sciences, and geographical databases. Twenty cross-sectional and 13 quasi-experimental studies published between 1996 and 2010 were included in the review. Results Most associations between the built environment and physical activity were in the expected direction or null. Land use mix, connectivity and population density and overall neighborhood design were however, important determinants of physical activity. The built environment was more likely to be associated with transportation walking compared with other types of physical activity including recreational walking. Three studies found an attenuation in associations between built environment characteristics and physical activity after accounting for neighborhood self-selection. Conclusion More quasi-experiments that examine a broader range of environmental attributes in relation to context-specific physical activity and that measure changes in the built environment, neighborhood preferences and their effect on physical activity are needed. PMID:22077952

Non-equilibrium statistical mechanics theory for the large scales of geophysical flows

NASA Astrophysics Data System (ADS)

Eric, S.; Bouchet, F.

2010-12-01

The aim of any theory of turbulence is to understand the statistical properties of the velocity field. As a huge number of degrees of freedom is involved, statistical mechanics is a natural approach. The self-organization of two-dimensional and geophysical turbulent flows is addressed based on statistical mechanics methods. We discuss classical and recent works on this subject; from the statistical mechanics basis of the theory up to applications to Jupiter’s troposphere and ocean vortices and jets. The equilibrium microcanonical measure is built from the Liouville theorem. Important statistical mechanics concepts (large deviations, mean field approach) and thermodynamic concepts (ensemble inequivalence, negative heat capacity) are briefly explained and used to predict statistical equilibria for turbulent flows. This is applied to make quantitative models of two-dimensional turbulence, the Great Red Spot and other Jovian vortices, ocean jets like the Gulf-Stream, and ocean vortices. A detailed comparison between these statistical equilibria and real flow observations will be discussed. We also present recent results for non-equilibrium situations, for which forces and dissipation are in a statistical balance. As an example, the concept of phase transition allows us to describe drastic changes of the whole system when a few external parameters are changed. F. Bouchet and E. Simonnet, Random Changes of Flow Topology in Two-Dimensional and Geophysical Turbulence, Physical Review Letters 102 (2009), no. 9, 094504-+. F. Bouchet and J. Sommeria, Emergence of intense jets and Jupiter's Great Red Spot as maximum-entropy structures, Journal of Fluid Mechanics 464 (2002), 165-207. A. Venaille and F. Bouchet, Ocean rings and jets as statistical equilibrium states, submitted to JPO F. Bouchet and A. Venaille, Statistical mechanics of two-dimensional and geophysical flows, submitted to Physics Reports Non-equilibrium phase transitions for the 2D Navier-Stokes equations with stochastic forces (time series and probability density functions (PDFs) of the modulus of the largest scale Fourrier component, showing bistability between dipole and unidirectional flows). This bistability is predicted by statistical mechanics.

Middle School Students' Perceptions of Safety: A Mixed-Methods Study.

PubMed

Sweeney, Shannon M; Von Hagen, Leigh Ann

2015-10-01

Active travel to school has been on the decline, despite its beneficial influence on children's current and future well-being. Adults' safety perceptions have been shown to influence children's active travel. Children's perceptions, particularly of safety, may be an important link not only to their present health and travel behaviors, but also their future health and behaviors. This study examined middle school students' perceptions of the built environment and safety. Overall, 776 students from 3 schools in Hudson County, New Jersey participated in a visual survey and structured, interactive classroom discussions. Emergent themes from the discussions were tested using multivariate statistical models. Findings suggest that older students, boys, and students who self-identified as black, rated built environment scenes as safer. Students also perceived being near adults, traveling in a group, and using crosswalks as significantly safer and want additional recognition of these to further improve safety. Students perceived that being near a school, in daylight, and aesthetics as factors contributing to safety. Schools and municipalities may increase programs for students to travel in groups, prioritize maintenance in school zones, and increase the number of crossing guards, particularly outside the immediate school proximity to further improve safety. © 2015, American School Health Association.

Dizeez: An Online Game for Human Gene-Disease Annotation

PubMed Central

Loguercio, Salvatore; Good, Benjamin M.; Su, Andrew I.

2013-01-01

Structured gene annotations are a foundation upon which many bioinformatics and statistical analyses are built. However the structured annotations available in public databases are a sparse representation of biological knowledge as a whole. The rate of biomedical data generation is such that centralized biocuration efforts struggle to keep up. New models for gene annotation need to be explored that expand the pace at which we are able to structure biomedical knowledge. Recently, online games have emerged as an effective way to recruit, engage and organize large numbers of volunteers to help address difficult biological challenges. For example, games have been successfully developed for protein folding (Foldit), multiple sequence alignment (Phylo) and RNA structure design (EteRNA). Here we present Dizeez, a simple online game built with the purpose of structuring knowledge of gene-disease associations. Preliminary results from game play online and at scientific conferences suggest that Dizeez is producing valid gene-disease annotations not yet present in any public database. These early results provide a basic proof of principle that online games can be successfully applied to the challenge of gene annotation. Dizeez is available at http://genegames.org. PMID:23951102

Nesting behavior of house mice (Mus domesticus) selected for increased wheel-running activity.

PubMed

Carter, P A; Swallow, J G; Davis, S J; Garland, T

2000-03-01

Nest building was measured in "active" (housed with access to running wheels) and "sedentary" (without wheel access) mice (Mus domesticus) from four replicate lines selected for 10 generations for high voluntary wheel-running behavior, and from four randombred control lines. Based on previous studies of mice bidirectionally selected for thermoregulatory nest building, it was hypothesized that nest building would show a negative correlated response to selection on wheel-running. Such a response could constrain the evolution of high voluntary activity because nesting has also been shown to be positively genetically correlated with successful production of weaned pups. With wheel access, selected mice of both sexes built significantly smaller nests than did control mice. Without wheel access, selected females also built significantly smaller nests than did control females, but only when body mass was excluded from the statistical model, suggesting that body mass mediated this correlated response to selection. Total distance run and mean running speed on wheels was significantly higher in selected mice than in controls, but no differences in amount of time spent running were measured, indicating a complex cause of the response of nesting to selection for voluntary wheel running.

Modelling high data rate communication network access protocol

NASA Technical Reports Server (NTRS)

Khanna, S.; Foudriat, E. C.; Paterra, Frank; Maly, Kurt J.; Overstreet, C. Michael

1990-01-01

Modeling of high data rate communication systems is different from the low data rate systems. Three simulations were built during the development phase of Carrier Sensed Multiple Access/Ring Network (CSMA/RN) modeling. The first was a model using SIMCRIPT based upon the determination and processing of each event at each node. The second simulation was developed in C based upon isolating the distinct object that can be identified as the ring, the message, the node, and the set of critical events. The third model further identified the basic network functionality by creating a single object, the node which includes the set of critical events which occur at the node. The ring structure is implicit in the node structure. This model was also built in C. Each model is discussed and their features compared. It should be stated that the language used was mainly selected by the model developer because of his past familiarity. Further the models were not built with the intent to compare either structure or language but because the complexity of the problem and initial results contained obvious errors, so alternative models were built to isolate, determine, and correct programming and modeling errors. The CSMA/RN protocol is discussed in sufficient detail to understand modeling complexities. Each model is described along with its features and problems. The models are compared and concluding observations and remarks are presented.

3D-QSAR analysis of MCD inhibitors by CoMFA and CoMSIA.

PubMed

Pourbasheer, Eslam; Aalizadeh, Reza; Ebadi, Amin; Ganjali, Mohammad Reza

2015-01-01

Three-dimensional quantitative structure-activity relationship was developed for the series of compounds as malonyl-CoA decarboxylase antagonists (MCD) using the CoMFA and CoMSIA methods. The statistical parameters for CoMFA (q(2)=0.558, r(2)=0.841) and CoMSIA (q(2)= 0.615, r(2) = 0.870) models were derived based on 38 compounds as training set in the basis of the selected alignment. The external predictive abilities of the built models were evaluated by using the test set of nine compounds. From obtained results, the CoMSIA method was found to have highly predictive capability in comparison with CoMFA method. Based on the given results by CoMSIA and CoMFA contour maps, some features that can enhance the activity of compounds as MCD antagonists were introduced and used to design new compounds with better inhibition activity.

Predicting hospital visits from geo-tagged Internet search logs.

PubMed

Agarwal, Vibhu; Han, Lichy; Madan, Isaac; Saluja, Shaurya; Shidham, Aaditya; Shah, Nigam H

2016-01-01

The steady rise in healthcare costs has deprived over 45 million Americans of healthcare services (1, 2) and has encouraged healthcare providers to look for opportunities to improve their operational efficiency. Prior studies have shown that evidence of healthcare seeking intent in Internet searches correlates well with healthcare resource utilization. Given the ubiquitous nature of mobile Internet search, we hypothesized that analyzing geo-tagged mobile search logs could enable us to machine-learn predictors of future patient visits. Using a de-identified dataset of geo-tagged mobile Internet search logs, we mined text and location patterns that are predictors of healthcare resource utilization and built statistical models that predict the probability of a user's future visit to a medical facility. Our efforts will enable the development of innovative methods for modeling and optimizing the use of healthcare resources-a crucial prerequisite for securing healthcare access for everyone in the days to come.

Modeling Geomagnetic Variations using a Machine Learning Framework

NASA Astrophysics Data System (ADS)

Cheung, C. M. M.; Handmer, C.; Kosar, B.; Gerules, G.; Poduval, B.; Mackintosh, G.; Munoz-Jaramillo, A.; Bobra, M.; Hernandez, T.; McGranaghan, R. M.

2017-12-01

We present a framework for data-driven modeling of Heliophysics time series data. The Solar Terrestrial Interaction Neural net Generator (STING) is an open source python module built on top of state-of-the-art statistical learning frameworks (traditional machine learning methods as well as deep learning). To showcase the capability of STING, we deploy it for the problem of predicting the temporal variation of geomagnetic fields. The data used includes solar wind measurements from the OMNI database and geomagnetic field data taken by magnetometers at US Geological Survey observatories. We examine the predictive capability of different machine learning techniques (recurrent neural networks, support vector machines) for a range of forecasting times (minutes to 12 hours). STING is designed to be extensible to other types of data. We show how STING can be used on large sets of data from different sensors/observatories and adapted to tackle other problems in Heliophysics.

Time-domain Surveys and Data Shift: Case Study at the intermediate Palomar Transient Factory

NASA Astrophysics Data System (ADS)

Rebbapragada, Umaa; Bue, Brian; Wozniak, Przemyslaw R.

2015-01-01

Next generation time-domain surveys are susceptible to the problem of data shift that is caused by upgrades to data processing pipelines and instruments. Data shift degrades the performance of automated machine learning classifiers that vet detections and classify source types because fundamental assumptions are violated when classifiers are built in one data regime but are deployed on data from another. This issue is not currently discussed within the astronomical community, but will be increasingly pressing over the next decade with the advent of new time domain surveys.We look at the problem of data shift that was caused by a data pipeline upgrade when the intermediate Palomar Transient Factory (iPTF) succeeded the Palomar Transient Factory (PTF) in January 2013. iPTF relies upon machine-learned Real-Bogus classifiers to vet sources extracted from subtracted images on a scale of zero to one where zero indicates a bogus (image artifact) and one indicates a real astronomical transient, with the overwhelming majority of candidates are scored as bogus. An effective Real-Bogus system filters all but the most promising candidates, which are presented to human scanners who make decisions about triggering follow up assets.The Real-Bogus systems currently in operation at iPTF (RB4 and RB5) solve the data shift problem. The statistical models of RB4 and RB5 were built from the ground up using examples from iPTF alone, whereas an older system, RB2, was built using PTF data, but was deployed after iPTF launched. We discuss the machine learning assumptions that are violated when a system is trained on one domain (PTF) but deployed on another (iPTF) that experiences data shift. We provide illustrative examples of data parameters and statistics that experienced shift. Finally, we show results comparing the three systems in operation, demonstrating that systems that solve domain shift (RB4 and RB5) are superior to those that don't (RB2).Research described in this abstract was carried out at the Jet Propulsion Laboratory under contract with the National Aeronautics and Space Administration. US Government Support Acknowledged.

Data-driven reduced order models for effective yield strength and partitioning of strain in multiphase materials

NASA Astrophysics Data System (ADS)

Latypov, Marat I.; Kalidindi, Surya R.

2017-10-01

There is a critical need for the development and verification of practically useful multiscale modeling strategies for simulating the mechanical response of multiphase metallic materials with heterogeneous microstructures. In this contribution, we present data-driven reduced order models for effective yield strength and strain partitioning in such microstructures. These models are built employing the recently developed framework of Materials Knowledge Systems that employ 2-point spatial correlations (or 2-point statistics) for the quantification of the heterostructures and principal component analyses for their low-dimensional representation. The models are calibrated to a large collection of finite element (FE) results obtained for a diverse range of microstructures with various sizes, shapes, and volume fractions of the phases. The performance of the models is evaluated by comparing the predictions of yield strength and strain partitioning in two-phase materials with the corresponding predictions from a classical self-consistent model as well as results of full-field FE simulations. The reduced-order models developed in this work show an excellent combination of accuracy and computational efficiency, and therefore present an important advance towards computationally efficient microstructure-sensitive multiscale modeling frameworks.

Earth Observations, Models and Geo-Design in Support of SDG Implementation and Monitoring

NASA Astrophysics Data System (ADS)

Plag, H. P.; Jules-Plag, S.

2016-12-01

Implementation and Monitoring of the United Nations' Sustainable Development Goals (SDGs) requires support from Earth observation and scientific communities. Applying a goal-based approach to determine the data needs to the Targets and Indicators associated with the SDGs demonstrates that integration of environmental with socio-economic and statistical data is required. Large data gaps exist for the built environment. A Geo-Design platform can provide the infrastructure and conceptual model for the data integration. The development of policies and actions to foster the implementation of SDGs in many cases requires research and the development of tools to answer "what if" questions. Here, agent-based models and model webs combined with a Geo-Design platform are promising avenues. This advanced combined infrastructure can also play a crucial role in the necessary capacity building. We will use the example of SDG 5 (Gender equality) to illustrate these approaches. SDG 11 (Sustainable Cities and Communities) is used to underline the cross-goal linkages and the joint benefits of Earth observations, data integration, and modeling tools for multiple SDGs.

Continuous Evaluation of Fast Processes in Climate Models Using ARM Measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Zhijin; Sha, Feng; Liu, Yangang

2016-02-02

This five-year award supports the project “Continuous Evaluation of Fast Processes in Climate Models Using ARM Measurements (FASTER)”. The goal of this project is to produce accurate, consistent and comprehensive data sets for initializing both single column models (SCMs) and cloud resolving models (CRMs) using data assimilation. A multi-scale three-dimensional variational data assimilation scheme (MS-3DVAR) has been implemented. This MS-3DVAR system is built on top of WRF/GSI. The Community Gridpoint Statistical Interpolation (GSI) system is an operational data assimilation system at the National Centers for Environmental Prediction (NCEP) and has been implemented in the Weather Research and Forecast (WRF) model.more » This MS-3DVAR is further enhanced by the incorporation of a land surface 3DVAR scheme and a comprehensive aerosol 3DVAR scheme. The data assimilation implementation focuses in the ARM SGP region. ARM measurements are assimilated along with other available satellite and radar data. Reanalyses are then generated for a few selected period of time. This comprehensive data assimilation system has also been employed for other ARM-related applications.« less

Model-based POD study of manual ultrasound inspection and sensitivity analysis using metamodel

NASA Astrophysics Data System (ADS)

Ribay, Guillemette; Artusi, Xavier; Jenson, Frédéric; Reece, Christopher; Lhuillier, Pierre-Emile

2016-02-01

The reliability of NDE can be quantified by using the Probability of Detection (POD) approach. Former studies have shown the potential of the model-assisted POD (MAPOD) approach to replace expensive experimental determination of POD curves. In this paper, we make use of CIVA software to determine POD curves for a manual ultrasonic inspection of a heavy component, for which a whole experimental POD campaign was not available. The influential parameters were determined by expert analysis. The semi-analytical models used in CIVA for wave propagation and beam-defect interaction have been validated in the range of variation of the influential parameters by comparison with finite element modelling (Athena). The POD curves are computed for « hit/miss » and « â versus a » analysis. The verification of Berens hypothesis is evaluated by statistical tools. A sensitivity study is performed to measure the relative influence of parameters on the defect response amplitude variance, using the Sobol sensitivity index. A meta-model is also built to reduce computing cost and enhance the precision of estimated index.

Additive interaction between heterogeneous environmental quality domains (air, water, land, sociodemographic and built environment) on preterm birth

EPA Science Inventory

BACKGROUND Environmental exposures often occur in tandem; however, epidemiological research often focuses on singular exposures. Statistical interactions among broad, well-characterized environmental domains have not yet been evaluated in association with health. We address this ...

Data exploration of social client relationship management (CRM 2.0) adoption in the Nigerian construction business.

PubMed

Ojelabi, Rapheal A; Afolabi, Adedeji O; Oyeyipo, Opeyemi O; Tunji-Olayeni, Patience F; Adewale, Bukola A

2018-06-01

Integrating social client relationship management (CRM 2.0) in the built environment can enhance the relationship between construction organizations and client towards sustaining a long and lasting collaboration. The data exploration analyzed the e-readiness of contracting and consulting construction firms in the uptake of CRM 2.0 and the barriers encountered in the adoption of the modern business tool. The targeted organizations consist of seventy five (75) construction businesses operating in Lagos State which were selected from a pool of registered contracting and consulting construction firms using random sampling technique. Descriptive statistics of the e-readiness of contracting and consulting construction firms for CRM 2.0 adoption and barriers limiting its uptake were analyzed. Also, inferential analysis using Mann-Whitney U statistical and independent sample t-test was performed on the dataset obtained. The data generated will support construction firms on the necessity to engage in client social relationship management in ensuring sustainable client relationship management in the built environment.

Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore.

PubMed

Shi, Yuan; Liu, Xu; Kok, Suet-Yheng; Rajarethinam, Jayanthi; Liang, Shaohong; Yap, Grace; Chong, Chee-Seng; Lee, Kim-Sung; Tan, Sharon S Y; Chin, Christopher Kuan Yew; Lo, Andrew; Kong, Waiming; Ng, Lee Ching; Cook, Alex R

2016-09-01

With its tropical rainforest climate, rapid urbanization, and changing demography and ecology, Singapore experiences endemic dengue; the last large outbreak in 2013 culminated in 22,170 cases. In the absence of a vaccine on the market, vector control is the key approach for prevention. We sought to forecast the evolution of dengue epidemics in Singapore to provide early warning of outbreaks and to facilitate the public health response to moderate an impending outbreak. We developed a set of statistical models using least absolute shrinkage and selection operator (LASSO) methods to forecast the weekly incidence of dengue notifications over a 3-month time horizon. This forecasting tool used a variety of data streams and was updated weekly, including recent case data, meteorological data, vector surveillance data, and population-based national statistics. The forecasting methodology was compared with alternative approaches that have been proposed to model dengue case data (seasonal autoregressive integrated moving average and step-down linear regression) by fielding them on the 2013 dengue epidemic, the largest on record in Singapore. Operationally useful forecasts were obtained at a 3-month lag using the LASSO-derived models. Based on the mean average percentage error, the LASSO approach provided more accurate forecasts than the other methods we assessed. We demonstrate its utility in Singapore's dengue control program by providing a forecast of the 2013 outbreak for advance preparation of outbreak response. Statistical models built using machine learning methods such as LASSO have the potential to markedly improve forecasting techniques for recurrent infectious disease outbreaks such as dengue. Shi Y, Liu X, Kok SY, Rajarethinam J, Liang S, Yap G, Chong CS, Lee KS, Tan SS, Chin CK, Lo A, Kong W, Ng LC, Cook AR. 2016. Three-month real-time dengue forecast models: an early warning system for outbreak alerts and policy decision support in Singapore. Environ Health Perspect 124:1369-1375; http://dx.doi.org/10.1289/ehp.1509981.

Empirical prediction of the onset dates of South China Sea summer monsoon

NASA Astrophysics Data System (ADS)

Zhu, Zhiwei; Li, Tim

2017-03-01

The onset of South China Sea summer monsoon (SCSSM) signifies the commencement of the wet season over East Asia. Predicting the SCSSM onset date is of significant importance. In this study, we establish two different statistical models, namely the physical-empirical model (PEM) and the spatial-temporal projection model (STPM) to predict the SCSSM onset. The PEM is constructed from the seasonal prediction perspective. Observational diagnoses reveal that the early onset of the SCSSM is preceded by (a) a warming tendency in middle and lower troposphere (850-500 hPa) over central Siberia from January to March, (b) a La Niña-like zonal dipole sea surface temperature pattern over the tropical Pacific in March, and (c) a dipole sea level pressure pattern with negative center in subtropics and positive center over high latitude of Southern Hemisphere in January. The PEM built on these predictors achieves a cross-validated reforecast temporal correlation coefficient (TCC) skill of 0.84 for the period of 1979-2004, and an independent forecast TCC skill of 0.72 for the period 2005-2014. The STPM is built on the extended-range forecast perspective. Pentad data are used to predict a zonal wind index over the South China Sea region. Similar to PEM, the STPM is constructed using 1979-2004 data. Based on the forecasted zonal wind index, the independent forecast of the SCSSM onset dates achieves a TCC skill of 0.90 for 2005-2014. The STPM provides more detailed information for the intraseasonal evolution during the period of the SCSSM onset (pentad 25-35). The two models proposed herein are expected to facilitate the real-time prediction of the SCSSM onset.

Comparing the appropriate geographic region for assessing built environmental correlates with walking trips using different metrics and model approaches

PubMed Central

Tribby, Calvin P.; Miller, Harvey J.; Brown, Barbara B.; Smith, Ken R.; Werner, Carol M.

2017-01-01

There is growing international evidence that supportive built environments encourage active travel such as walking. An unsettled question is the role of geographic regions for analyzing the relationship between the built environment and active travel. This paper examines the geographic region question by assessing walking trip models that use two different regions: walking activity spaces and self-defined neighborhoods. We also use two types of built environment metrics, perceived and audit data, and two types of study design, cross-sectional and longitudinal, to assess these regions. We find that the built environment associations with walking are dependent on the type of metric and the type of model. Audit measures summarized within walking activity spaces better explain walking trips compared to audit measures within self-defined neighborhoods. Perceived measures summarized within self-defined neighborhoods have mixed results. Finally, results differ based on study design. This suggests that results may not be comparable among different regions, metrics and designs; researchers need to consider carefully these choices when assessing active travel correlates. PMID:28237743

Project risk management in the construction of high-rise buildings

NASA Astrophysics Data System (ADS)

Titarenko, Boris; Hasnaoui, Amir; Titarenko, Roman; Buzuk, Liliya

2018-03-01

This paper shows the project risk management methods, which allow to better identify risks in the construction of high-rise buildings and to manage them throughout the life cycle of the project. One of the project risk management processes is a quantitative analysis of risks. The quantitative analysis usually includes the assessment of the potential impact of project risks and their probabilities. This paper shows the most popular methods of risk probability assessment and tries to indicate the advantages of the robust approach over the traditional methods. Within the framework of the project risk management model a robust approach of P. Huber is applied and expanded for the tasks of regression analysis of project data. The suggested algorithms used to assess the parameters in statistical models allow to obtain reliable estimates. A review of the theoretical problems of the development of robust models built on the methodology of the minimax estimates was done and the algorithm for the situation of asymmetric "contamination" was developed.

Building up a QSAR model for toxicity toward Tetrahymena pyriformis by the Monte Carlo method: A case of benzene derivatives.

PubMed

Toropova, Alla P; Schultz, Terry W; Toropov, Andrey A

2016-03-01

Data on toxicity toward Tetrahymena pyriformis is indicator of applicability of a substance in ecologic and pharmaceutical aspects. Quantitative structure-activity relationships (QSARs) between the molecular structure of benzene derivatives and toxicity toward T. pyriformis (expressed as the negative logarithms of the population growth inhibition dose, mmol/L) are established. The available data were randomly distributed three times into the visible training and calibration sets, and invisible validation sets. The statistical characteristics for the validation set are the following: r(2)=0.8179 and s=0.338 (first distribution); r(2)=0.8682 and s=0.341 (second distribution); r(2)=0.8435 and s=0.323 (third distribution). These models are built up using only information on the molecular structure: no data on physicochemical parameters, 3D features of the molecular structure and quantum mechanics descriptors are involved in the modeling process. Copyright © 2016 Elsevier B.V. All rights reserved.

Statistical attribution analysis of the nonstationarity of the annual runoff series of the Weihe River.

PubMed

Xiong, Lihua; Jiang, Cong; Du, Tao

2014-01-01

Time-varying moments models based on Pearson Type III and normal distributions respectively are built under the generalized additive model in location, scale and shape (GAMLSS) framework to analyze the nonstationarity of the annual runoff series of the Weihe River, the largest tributary of the Yellow River. The detection of nonstationarities in hydrological time series (annual runoff, precipitation and temperature) from 1960 to 2009 is carried out using a GAMLSS model, and then the covariate analysis for the annual runoff series is implemented with GAMLSS. Finally, the attribution of each covariate to the nonstationarity of annual runoff is analyzed quantitatively. The results demonstrate that (1) obvious change-points exist in all three hydrological series, (2) precipitation, temperature and irrigated area are all significant covariates of the annual runoff series, and (3) temperature increase plays the main role in leading to the reduction of the annual runoff series in the study basin, followed by the decrease of precipitation and the increase of irrigated area.

Quantum image encryption based on restricted geometric and color transformations

NASA Astrophysics Data System (ADS)

Song, Xian-Hua; Wang, Shen; Abd El-Latif, Ahmed A.; Niu, Xia-Mu

2014-08-01

A novel encryption scheme for quantum images based on restricted geometric and color transformations is proposed. The new strategy comprises efficient permutation and diffusion properties for quantum image encryption. The core idea of the permutation stage is to scramble the codes of the pixel positions through restricted geometric transformations. Then, a new quantum diffusion operation is implemented on the permutated quantum image based on restricted color transformations. The encryption keys of the two stages are generated by two sensitive chaotic maps, which can ensure the security of the scheme. The final step, measurement, is built by the probabilistic model. Experiments conducted on statistical analysis demonstrate that significant improvements in the results are in favor of the proposed approach.

Residential segregation, health behavior and overweight/obesity among a national sample of African American adults.

PubMed

Corral, Irma; Landrine, Hope; Hao, Yongping; Zhao, Luhua; Mellerson, Jenelle L; Cooper, Dexter L

2012-04-01

We examined the role of residential segregation in 5+ daily fruit/vegetable consumption, exercise, and overweight/obesity among African Americans by linking data on the 11,142 African American adults in the 2000 Behavioral Risk Factor Surveillance System to 2000 census data on the segregation of metropolitan statistical areas (MSAs). Multi-level modeling revealed that after controlling for individual-level variables, MSA Segregation and Poverty contributed to fruit/vegetable consumption, MSA Poverty alone contributed to exercise, and MSA Segregation alone contributed to overweight/obesity. These findings highlight the need for research on the built-environments of the segregated neighborhoods in which most African Americans reside, and suggest that neighborhood disparities may contribute to health disparities.

A statistical learning strategy for closed-loop control of fluid flows

NASA Astrophysics Data System (ADS)

Guéniat, Florimond; Mathelin, Lionel; Hussaini, M. Yousuff

2016-12-01

This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied to the sensor measurements from which a Markov process model is derived, approximating the complex system's dynamics. A control strategy is then learned using reinforcement learning once rewards relevant with respect to the control objective are identified. This method is designed for experimental configurations, requiring no computations nor prior knowledge of the system, and enjoys intrinsic robustness. It is illustrated on two systems: the control of the transitions of a Lorenz'63 dynamical system, and the control of the drag of a cylinder flow. The method is shown to perform well.

A Partial Least Square Approach for Modeling Gene-gene and Gene-environment Interactions When Multiple Markers Are Genotyped

PubMed Central

Wang, Tao; Ho, Gloria; Ye, Kenny; Strickler, Howard; Elston, Robert C.

2008-01-01

Genetic association studies achieve an unprecedented level of resolution in mapping disease genes by genotyping dense SNPs in a gene region. Meanwhile, these studies require new powerful statistical tools that can optimally handle a large amount of information provided by genotype data. A question that arises is how to model interactions between two genes. Simply modeling all possible interactions between the SNPs in two gene regions is not desirable because a greatly increased number of degrees of freedom can be involved in the test statistic. We introduce an approach to reduce the genotype dimension in modeling interactions. The genotype compression of this approach is built upon the information on both the trait and the cross-locus gametic disequilibrium between SNPs in two interacting genes, in such a way as to parsimoniously model the interactions without loss of useful information in the process of dimension reduction. As a result, it improves power to detect association in the presence of gene-gene interactions. This approach can be similarly applied for modeling gene-environment interactions. We compare this method with other approaches: the corresponding test without modeling any interaction, that based on a saturated interaction model, that based on principal component analysis, and that based on Tukey’s 1-df model. Our simulations suggest that this new approach has superior power to that of the other methods. In an application to endometrial cancer case-control data from the Women’s Health Initiative (WHI), this approach detected AKT1 and AKT2 as being significantly associated with endometrial cancer susceptibility by taking into account their interactions with BMI. PMID:18615621

A partial least-square approach for modeling gene-gene and gene-environment interactions when multiple markers are genotyped.

PubMed

Wang, Tao; Ho, Gloria; Ye, Kenny; Strickler, Howard; Elston, Robert C

2009-01-01

Genetic association studies achieve an unprecedented level of resolution in mapping disease genes by genotyping dense single nucleotype polymorphisms (SNPs) in a gene region. Meanwhile, these studies require new powerful statistical tools that can optimally handle a large amount of information provided by genotype data. A question that arises is how to model interactions between two genes. Simply modeling all possible interactions between the SNPs in two gene regions is not desirable because a greatly increased number of degrees of freedom can be involved in the test statistic. We introduce an approach to reduce the genotype dimension in modeling interactions. The genotype compression of this approach is built upon the information on both the trait and the cross-locus gametic disequilibrium between SNPs in two interacting genes, in such a way as to parsimoniously model the interactions without loss of useful information in the process of dimension reduction. As a result, it improves power to detect association in the presence of gene-gene interactions. This approach can be similarly applied for modeling gene-environment interactions. We compare this method with other approaches, the corresponding test without modeling any interaction, that based on a saturated interaction model, that based on principal component analysis, and that based on Tukey's one-degree-of-freedom model. Our simulations suggest that this new approach has superior power to that of the other methods. In an application to endometrial cancer case-control data from the Women's Health Initiative, this approach detected AKT1 and AKT2 as being significantly associated with endometrial cancer susceptibility by taking into account their interactions with body mass index.

Nuclear reactors built, being built, or planned, 1994

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1995-07-01

This document contains unclassified information about facilities built, being built, or planned in the United States for domestic use or export as of December 31, 1994. The Office of Scientific and Technical Information, US Department of Energy, gathers this information annually from Washington headquarters and field offices of DOE; from the US Nuclear Regulatory Commission (NRC); from the US reactor manufacturers who are the principal nuclear contractors for foreign reactor locations; from US and foreign embassies; and from foreign governmental nuclear departments. The book consists of three divisions, as follows: a commercial reactor locator map and tables of the characteristicmore » and statistical data that follow; a table of abbreviations; tables of data for reactors operating, being built, or planned; and tables of data for reactors that have been shut down permanently or dismantled. The reactors are subdivided into the following parts: Civilian, Production, Military, Export, and Critical Assembly. Export reactor refers to a reactor for which the principal nuclear contractor is a US company -- working either independently or in cooperation with a foreign company (Part 4). Critical assembly refers to an assembly of fuel and moderator that requires an external source of neutrons to initiate and maintain fission. A critical assembly is used for experimental measurements (Part 5).« less

Nuclear reactors built, being built, or planned: 1995

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1996-08-01

This report contains unclassified information about facilities built, being built, or planned in the US for domestic use or export as of December 31, 1995. The Office of Scientific and Technical Information, US Department of Energy, gathers this information annually from Washington headquarters and field offices of DOE; from the US Nuclear Regulatory Commission (NRC); from the US reactor manufacturers who are the principal nuclear contractors for foreign reactor locations; from US and foreign embassies; and from foreign governmental nuclear departments. The book consists of three divisions, as follows: (1) a commercial reactor locator map and tables of the characteristicmore » and statistical data that follow; a table of abbreviations; (2) tables of data for reactors operating, being built, or planned; and (3) tables of data for reactors that have been shut down permanently or dismantled. The reactors are subdivided into the following parts: Civilian, Production, Military, Export, and Critical Assembly. Export reactor refers to a reactor for which the principal nuclear contractor is a US company--working either independently or in cooperation with a foreign company (Part 4). Critical assembly refers to an assembly of fuel and moderator that requires an external source of neutrons to initiate and maintain fission. A critical assembly is used for experimental measurements (Part 5).« less

Statistical patterns in the location of natural lightning

NASA Astrophysics Data System (ADS)

Zoghzoghy, F. G.; Cohen, M. B.; Said, R. K.; Inan, U. S.

2013-01-01

Lightning discharges are nature's way of neutralizing the electrical buildup in thunderclouds. Thus, if an individual discharge destroys a substantial fraction of the cloud charge, the probability of a subsequent flash is reduced until the cloud charge separation rebuilds. The temporal pattern of lightning activity in a localized region may thus inherently be a proxy measure of the corresponding timescales for charge separation and electric field buildup processes. We present a statistical technique to bring out this effect (as well as the subsequent recovery) using lightning geo-location data, in this case with data from the National Lightning Detection Network (NLDN) and from the GLD360 Network. We use this statistical method to show that a lightning flash can remove an appreciable fraction of the built up charge, affecting the neighboring lightning activity for tens of seconds within a ˜ 10 km radius. We find that our results correlate with timescales of electric field buildup in storms and suggest that the proposed statistical tool could be used to study the electrification of storms on a global scale. We find that this flash suppression effect is a strong function of flash type, flash polarity, cloud-to-ground flash multiplicity, the geographic location of lightning, and is proportional to NLDN model-derived peak stroke current. We characterize the spatial and temporal extent of the suppression effect as a function of these parameters and discuss various applications of our findings.

QPROT: Statistical method for testing differential expression using protein-level intensity data in label-free quantitative proteomics.

PubMed

Choi, Hyungwon; Kim, Sinae; Fermin, Damian; Tsou, Chih-Chiang; Nesvizhskii, Alexey I

2015-11-03

We introduce QPROT, a statistical framework and computational tool for differential protein expression analysis using protein intensity data. QPROT is an extension of the QSPEC suite, originally developed for spectral count data, adapted for the analysis using continuously measured protein-level intensity data. QPROT offers a new intensity normalization procedure and model-based differential expression analysis, both of which account for missing data. Determination of differential expression of each protein is based on the standardized Z-statistic based on the posterior distribution of the log fold change parameter, guided by the false discovery rate estimated by a well-known Empirical Bayes method. We evaluated the classification performance of QPROT using the quantification calibration data from the clinical proteomic technology assessment for cancer (CPTAC) study and a recently published Escherichia coli benchmark dataset, with evaluation of FDR accuracy in the latter. QPROT is a statistical framework with computational software tool for comparative quantitative proteomics analysis. It features various extensions of QSPEC method originally built for spectral count data analysis, including probabilistic treatment of missing values in protein intensity data. With the increasing popularity of label-free quantitative proteomics data, the proposed method and accompanying software suite will be immediately useful for many proteomics laboratories. This article is part of a Special Issue entitled: Computational Proteomics. Copyright © 2015 Elsevier B.V. All rights reserved.

Statistical analysis of porosity of 17-4PH alloy processed by selective laser melting

NASA Astrophysics Data System (ADS)

Ponnusamy, P.; Masood, S. H.; Ruan, D.; Palanisamy, S.; Mohamed, O. A.

2017-07-01

Selective Laser Melting (SLM) is a powder-bed type Additive Manufacturing (AM) process, where parts are built layer-by-layer by laser melting of powder layers of metal. There are several SLM process parameters that affect the accuracy and quality of the metal parts produced by SLM. Therefore, it is essential to understand the effect of these parameters on the quality and properties of the parts built by this process. In this paper, using Taguchi design of experiments, the effect of four SLM process parameters namely laser power, defocus distance, layer thickness and build orientation are considered on the porosity of 17-4PH stainless steel parts built on ProX200 SLM direct metal printer. The porositywas found to be optimum at a defocus distance of -4mm and a laser power of 240 W with a layer thickness of 30 μm and using vertical build orientation.

Accuracy and Calibration of Computational Approaches for Inpatient Mortality Predictive Modeling.

PubMed

Nakas, Christos T; Schütz, Narayan; Werners, Marcus; Leichtle, Alexander B

2016-01-01

Electronic Health Record (EHR) data can be a key resource for decision-making support in clinical practice in the "big data" era. The complete database from early 2012 to late 2015 involving hospital admissions to Inselspital Bern, the largest Swiss University Hospital, was used in this study, involving over 100,000 admissions. Age, sex, and initial laboratory test results were the features/variables of interest for each admission, the outcome being inpatient mortality. Computational decision support systems were utilized for the calculation of the risk of inpatient mortality. We assessed the recently proposed Acute Laboratory Risk of Mortality Score (ALaRMS) model, and further built generalized linear models, generalized estimating equations, artificial neural networks, and decision tree systems for the predictive modeling of the risk of inpatient mortality. The Area Under the ROC Curve (AUC) for ALaRMS marginally corresponded to the anticipated accuracy (AUC = 0.858). Penalized logistic regression methodology provided a better result (AUC = 0.872). Decision tree and neural network-based methodology provided even higher predictive performance (up to AUC = 0.912 and 0.906, respectively). Additionally, decision tree-based methods can efficiently handle Electronic Health Record (EHR) data that have a significant amount of missing records (in up to >50% of the studied features) eliminating the need for imputation in order to have complete data. In conclusion, we show that statistical learning methodology can provide superior predictive performance in comparison to existing methods and can also be production ready. Statistical modeling procedures provided unbiased, well-calibrated models that can be efficient decision support tools for predicting inpatient mortality and assigning preventive measures.

Invariant visual object recognition: a model, with lighting invariance.

PubMed

Rolls, Edmund T; Stringer, Simon M

2006-01-01

How are invariant representations of objects formed in the visual cortex? We describe a neurophysiological and computational approach which focusses on a feature hierarchy model in which invariant representations can be built by self-organizing learning based on the statistics of the visual input. The model can use temporal continuity in an associative synaptic learning rule with a short term memory trace, and/or it can use spatial continuity in Continuous Transformation learning. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and in this paper we show also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in for example spatial and object search tasks. The model has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene.

Part 2: Forensic attribution profiling of Russian VX in food using liquid chromatography-mass spectrometry.

PubMed

Jansson, Daniel; Lindström, Susanne Wiklund; Norlin, Rikard; Hok, Saphon; Valdez, Carlos A; Williams, Audrey M; Alcaraz, Armando; Nilsson, Calle; Åstot, Crister

2018-08-15

This work is part two of a three-part series in this issue of a Sweden-United States collaborative effort towards the understanding of the chemical attribution signatures of Russian VX (VR) in synthesized samples and complex food matrices. In this study, we describe the sourcing of VR present in food based on chemical analysis of attribution signatures by liquid chromatography-tandem mass spectrometry (LC-MS/MS) combined with multivariate data analysis. Analytical data was acquired from seven different foods spiked with VR batches that were synthesized via six different routes in two separate laboratories. The synthesis products were spiked at a lethal dose into seven food matrices: water, orange juice, apple purée, baby food, pea purée, liquid eggs and hot dog. After acetonitrile sample extraction, the samples were analyzed by LC-MS/MS operated in MRM mode. A multivariate statistical calibration model was built on the chemical attribution profiles from 118 VR spiked food samples. Using the model, an external test-set of the six synthesis routes employed for VR production was correctly identified with no observable major impact of the food matrices to the classification. The overall performance of the statistical models was found to be exceptional (94%) for the test set samples retrospectively classified to their synthesis routes. Copyright © 2018 Elsevier B.V. All rights reserved.

Defect detection of castings in radiography images using a robust statistical feature.

PubMed

Zhao, Xinyue; He, Zaixing; Zhang, Shuyou

2014-01-01

One of the most commonly used optical methods for defect detection is radiographic inspection. Compared with methods that extract defects directly from the radiography image, model-based methods deal with the case of an object with complex structure well. However, detection of small low-contrast defects in nonuniformly illuminated images is still a major challenge for them. In this paper, we present a new method based on the grayscale arranging pairs (GAP) feature to detect casting defects in radiography images automatically. First, a model is built using pixel pairs with a stable intensity relationship based on the GAP feature from previously acquired images. Second, defects can be extracted by comparing the difference of intensity-difference signs between the input image and the model statistically. The robustness of the proposed method to noise and illumination variations has been verified on casting radioscopic images with defects. The experimental results showed that the average computation time of the proposed method in the testing stage is 28 ms per image on a computer with a Pentium Core 2 Duo 3.00 GHz processor. For the comparison, we also evaluated the performance of the proposed method as well as that of the mixture-of-Gaussian-based and crossing line profile methods. The proposed method achieved 2.7% and 2.0% false negative rates in the noise and illumination variation experiments, respectively.

Cross-frequency and band-averaged response variance prediction in the hybrid deterministic-statistical energy analysis method

NASA Astrophysics Data System (ADS)

Reynders, Edwin P. B.; Langley, Robin S.

2018-08-01

The hybrid deterministic-statistical energy analysis method has proven to be a versatile framework for modeling built-up vibro-acoustic systems. The stiff system components are modeled deterministically, e.g., using the finite element method, while the wave fields in the flexible components are modeled as diffuse. In the present paper, the hybrid method is extended such that not only the ensemble mean and variance of the harmonic system response can be computed, but also of the band-averaged system response. This variance represents the uncertainty that is due to the assumption of a diffuse field in the flexible components of the hybrid system. The developments start with a cross-frequency generalization of the reciprocity relationship between the total energy in a diffuse field and the cross spectrum of the blocked reverberant loading at the boundaries of that field. By making extensive use of this generalization in a first-order perturbation analysis, explicit expressions are derived for the cross-frequency and band-averaged variance of the vibrational energies in the diffuse components and for the cross-frequency and band-averaged variance of the cross spectrum of the vibro-acoustic field response of the deterministic components. These expressions are extensively validated against detailed Monte Carlo analyses of coupled plate systems in which diffuse fields are simulated by randomly distributing small point masses across the flexible components, and good agreement is found.

Evaluation of a statistics-based Ames mutagenicity QSAR model and interpretation of the results obtained.

PubMed

Barber, Chris; Cayley, Alex; Hanser, Thierry; Harding, Alex; Heghes, Crina; Vessey, Jonathan D; Werner, Stephane; Weiner, Sandy K; Wichard, Joerg; Giddings, Amanda; Glowienke, Susanne; Parenty, Alexis; Brigo, Alessandro; Spirkl, Hans-Peter; Amberg, Alexander; Kemper, Ray; Greene, Nigel

2016-04-01

The relative wealth of bacterial mutagenicity data available in the public literature means that in silico quantitative/qualitative structure activity relationship (QSAR) systems can readily be built for this endpoint. A good means of evaluating the performance of such systems is to use private unpublished data sets, which generally represent a more distinct chemical space than publicly available test sets and, as a result, provide a greater challenge to the model. However, raw performance metrics should not be the only factor considered when judging this type of software since expert interpretation of the results obtained may allow for further improvements in predictivity. Enough information should be provided by a QSAR to allow the user to make general, scientifically-based arguments in order to assess and overrule predictions when necessary. With all this in mind, we sought to validate the performance of the statistics-based in vitro bacterial mutagenicity prediction system Sarah Nexus (version 1.1) against private test data sets supplied by nine different pharmaceutical companies. The results of these evaluations were then analysed in order to identify findings presented by the model which would be useful for the user to take into consideration when interpreting the results and making their final decision about the mutagenic potential of a given compound. Copyright © 2015 Elsevier Inc. All rights reserved.

Innocent Until Proven Guilty

ERIC Educational Resources Information Center

Case, Catherine; Whitaker, Douglas

2016-01-01

In the criminal justice system, defendants accused of a crime are presumed innocent until proven guilty. Statistical inference in any context is built on an analogous principle: The null hypothesis--often a hypothesis of "no difference" or "no effect"--is presumed true unless there is sufficient evidence against it. In this…

A Diagnostics Tool to detect ensemble forecast system anomaly and guide operational decisions

NASA Astrophysics Data System (ADS)

Park, G. H.; Srivastava, A.; Shrestha, E.; Thiemann, M.; Day, G. N.; Draijer, S.

2017-12-01

The hydrologic community is moving toward using ensemble forecasts to take uncertainty into account during the decision-making process. The New York City Department of Environmental Protection (DEP) implements several types of ensemble forecasts in their decision-making process: ensemble products for a statistical model (Hirsch and enhanced Hirsch); the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) forecasts based on the classical Ensemble Streamflow Prediction (ESP) technique; and the new NWS Hydrologic Ensemble Forecasting Service (HEFS) forecasts. To remove structural error and apply the forecasts to additional forecast points, the DEP post processes both the AHPS and the HEFS forecasts. These ensemble forecasts provide mass quantities of complex data, and drawing conclusions from these forecasts is time-consuming and difficult. The complexity of these forecasts also makes it difficult to identify system failures resulting from poor data, missing forecasts, and server breakdowns. To address these issues, we developed a diagnostic tool that summarizes ensemble forecasts and provides additional information such as historical forecast statistics, forecast skill, and model forcing statistics. This additional information highlights the key information that enables operators to evaluate the forecast in real-time, dynamically interact with the data, and review additional statistics, if needed, to make better decisions. We used Bokeh, a Python interactive visualization library, and a multi-database management system to create this interactive tool. This tool compiles and stores data into HTML pages that allows operators to readily analyze the data with built-in user interaction features. This paper will present a brief description of the ensemble forecasts, forecast verification results, and the intended applications for the diagnostic tool.

Comparative study of sea ice dynamics simulations with a Maxwell elasto-brittle rheology and the elastic-viscous-plastic rheology in NEMO-LIM3

NASA Astrophysics Data System (ADS)

Raulier, Jonathan; Dansereau, Véronique; Fichefet, Thierry; Legat, Vincent; Weiss, Jérôme

2017-04-01

Sea ice is a highly dynamical environment characterized by a dense mesh of fractures or leads, constantly opening and closing over short time scales. This characteristic geomorphology is linked to the existence of linear kinematic features, which consist of quasi-linear patterns emerging from the observed strain rate field of sea ice. Standard rheologies used in most state-of-the-art sea ice models, like the well-known elastic-viscous-plastic rheology, are thought to misrepresent those linear kinematic features and the observed statistical distribution of deformation rates. Dedicated rheologies built to catch the processes known to be at the origin of the formation of leads are developed but still need evaluations on the global scale. One of them, based on a Maxwell elasto-brittle formulation, is being integrated in the NEMO-LIM3 global ocean-sea ice model (www.nemo-ocean.eu; www.elic.ucl.ac.be/lim). In the present study, we compare the results of the sea ice model LIM3 obtained with two different rheologies: the elastic-viscous-plastic rheology commonly used in LIM3 and a Maxwell elasto-brittle rheology. This comparison is focused on the statistical characteristics of the simulated deformation rate and on the ability of the model to reproduce the existence of leads within the ice pack. The impact of the lead representation on fluxes between ice, atmosphere and ocean is also assessed.

3D printing PLGA: a quantitative examination of the effects of polymer composition and printing parameters on print resolution

PubMed Central

Guo, Ting; Holzberg, Timothy R; Lim, Casey G; Gao, Feng; Gargava, Ankit; Trachtenberg, Jordan E; Mikos, Antonios G; Fisher, John P

2018-01-01

In the past few decades, 3D printing has played a significant role in fabricating scaffolds with consistent, complex structure that meet patient-specific needs in future clinical applications. Although many studies have contributed to this emerging field of additive manufacturing, which includes material development and computer-aided scaffold design, current quantitative analyses do not correlate material properties, printing parameters, and printing outcomes to a great extent. A model that correlates these properties has tremendous potential to standardize 3D printing for tissue engineering and biomaterial science. In this study, we printed poly(lactic-co-glycolic acid) (PLGA) utilizing a direct melt extrusion technique without additional ingredients. We investigated PLGA with various lactic acid: glycolic acid (LA:GA) molecular weight ratios and end caps to demonstrate the dependence of the extrusion process on the polymer composition. Micro-computed tomography was then used to evaluate printed scaffolds containing different LA:GA ratios, composed of different fiber patterns, and processed under different printing conditions. We built a statistical model to reveal the correlation and predominant factors that determine printing precision. Our model showed a strong linear relationship between the actual and predicted precision under different combinations of printing conditions and material compositions. This quantitative examination establishes a significant foreground to 3D print biomaterials following a systematic fabrication procedure. Additionally, our proposed statistical models can be applied to couple specific biomaterials and 3D printing applications for patient implants with particular requirements. PMID:28244880

3D printing PLGA: a quantitative examination of the effects of polymer composition and printing parameters on print resolution.

PubMed

Guo, Ting; Holzberg, Timothy R; Lim, Casey G; Gao, Feng; Gargava, Ankit; Trachtenberg, Jordan E; Mikos, Antonios G; Fisher, John P

2017-04-12

In the past few decades, 3D printing has played a significant role in fabricating scaffolds with consistent, complex structure that meet patient-specific needs in future clinical applications. Although many studies have contributed to this emerging field of additive manufacturing, which includes material development and computer-aided scaffold design, current quantitative analyses do not correlate material properties, printing parameters, and printing outcomes to a great extent. A model that correlates these properties has tremendous potential to standardize 3D printing for tissue engineering and biomaterial science. In this study, we printed poly(lactic-co-glycolic acid) (PLGA) utilizing a direct melt extrusion technique without additional ingredients. We investigated PLGA with various lactic acid:glycolic acid (LA:GA) molecular weight ratios and end caps to demonstrate the dependence of the extrusion process on the polymer composition. Micro-computed tomography was then used to evaluate printed scaffolds containing different LA:GA ratios, composed of different fiber patterns, and processed under different printing conditions. We built a statistical model to reveal the correlation and predominant factors that determine printing precision. Our model showed a strong linear relationship between the actual and predicted precision under different combinations of printing conditions and material compositions. This quantitative examination establishes a significant foreground to 3D print biomaterials following a systematic fabrication procedure. Additionally, our proposed statistical models can be applied to couple specific biomaterials and 3D printing applications for patient implants with particular requirements.

Optimization Methods in Sherpa

NASA Astrophysics Data System (ADS)

Siemiginowska, Aneta; Nguyen, Dan T.; Doe, Stephen M.; Refsdal, Brian L.

2009-09-01

Forward fitting is a standard technique used to model X-ray data. A statistic, usually assumed weighted chi^2 or Poisson likelihood (e.g. Cash), is minimized in the fitting process to obtain a set of the best model parameters. Astronomical models often have complex forms with many parameters that can be correlated (e.g. an absorbed power law). Minimization is not trivial in such setting, as the statistical parameter space becomes multimodal and finding the global minimum is hard. Standard minimization algorithms can be found in many libraries of scientific functions, but they are usually focused on specific functions. However, Sherpa designed as general fitting and modeling application requires very robust optimization methods that can be applied to variety of astronomical data (X-ray spectra, images, timing, optical data etc.). We developed several optimization algorithms in Sherpa targeting a wide range of minimization problems. Two local minimization methods were built: Levenberg-Marquardt algorithm was obtained from MINPACK subroutine LMDIF and modified to achieve the required robustness; and Nelder-Mead simplex method has been implemented in-house based on variations of the algorithm described in the literature. A global search Monte-Carlo method has been implemented following a differential evolution algorithm presented by Storn and Price (1997). We will present the methods in Sherpa and discuss their usage cases. We will focus on the application to Chandra data showing both 1D and 2D examples. This work is supported by NASA contract NAS8-03060 (CXC).

1. MODEL OF THE EVELINA M. GOULART AS BUILT BY ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

1. MODEL OF THE EVELINA M. GOULART AS BUILT BY DR. WILLIAM G. HEISEY. IT DEPICTS THE GOULART AS SHE APPEARED IN THE SUMMER OF 1927 SHORTLY AFTER LAUNCHING AND RIGGED FOR SWORDFISHING. - Auxiliary Fishing Schooner "Evelina M. Goulart", Essex Shipbuilding Museum, 66 Main Street, Essex, Essex County, MA

Prediction of microstructure, residual stress, and deformation in laser powder bed fusion process

NASA Astrophysics Data System (ADS)

Yang, Y. P.; Jamshidinia, M.; Boulware, P.; Kelly, S. M.

2018-05-01

Laser powder bed fusion (L-PBF) process has been investigated significantly to build production parts with a complex shape. Modeling tools, which can be used in a part level, are essential to allow engineers to fine tune the shape design and process parameters for additive manufacturing. This study focuses on developing modeling methods to predict microstructure, hardness, residual stress, and deformation in large L-PBF built parts. A transient sequentially coupled thermal and metallurgical analysis method was developed to predict microstructure and hardness on L-PBF built high-strength, low-alloy steel parts. A moving heat-source model was used in this analysis to accurately predict the temperature history. A kinetics based model which was developed to predict microstructure in the heat-affected zone of a welded joint was extended to predict the microstructure and hardness in an L-PBF build by inputting the predicted temperature history. The tempering effect resulting from the following built layers on the current-layer microstructural phases were modeled, which is the key to predict the final hardness correctly. It was also found that the top layers of a build part have higher hardness because of the lack of the tempering effect. A sequentially coupled thermal and mechanical analysis method was developed to predict residual stress and deformation for an L-PBF build part. It was found that a line-heating model is not suitable for analyzing a large L-PBF built part. The layer heating method is a potential method for analyzing a large L-PBF built part. The experiment was conducted to validate the model predictions.

Building Construction Progress Monitoring Using Unmanned Aerial System (uas), Low-Cost Photogrammetry, and Geographic Information System (gis)

NASA Astrophysics Data System (ADS)

Bognot, J. R.; Candido, C. G.; Blanco, A. C.; Montelibano, J. R. Y.

2018-05-01

Monitoring the progress of building's construction is critical in construction management. However, measuring the building construction's progress are still manual, time consuming, error prone, and impose tedious process of analysis leading to delays, additional costings and effort. The main goal of this research is to develop a methodology for building construction progress monitoring based on 3D as-built model of the building from unmanned aerial system (UAS) images, 4D as-planned model (with construction schedule integrated) and, GIS analysis. Monitoring was done by capturing videos of the building with a camera-equipped UAS. Still images were extracted, filtered, bundle-adjusted, and 3D as-built model was generated using open source photogrammetric software. The as-planned model was generated from digitized CAD drawings using GIS. The 3D as-built model was aligned with the 4D as-planned model of building formed from extrusion of building elements, and integration of the construction's planned schedule. The construction progress is visualized via color-coding the building elements in the 3D model. The developed methodology was conducted and applied from the data obtained from an actual construction site. Accuracy in detecting `built' or `not built' building elements ranges from 82-84 % and precision of 50-72 %. Quantified progress in terms of the number of building elements are 21.31% (November 2016), 26.84 % (January 2017) and 44.19 % (March 2017). The results can be used as an input for progress monitoring performance of construction projects and improving related decision-making process.

Prediction of microstructure, residual stress, and deformation in laser powder bed fusion process

NASA Astrophysics Data System (ADS)

Yang, Y. P.; Jamshidinia, M.; Boulware, P.; Kelly, S. M.

2017-12-01

Laser powder bed fusion (L-PBF) process has been investigated significantly to build production parts with a complex shape. Modeling tools, which can be used in a part level, are essential to allow engineers to fine tune the shape design and process parameters for additive manufacturing. This study focuses on developing modeling methods to predict microstructure, hardness, residual stress, and deformation in large L-PBF built parts. A transient sequentially coupled thermal and metallurgical analysis method was developed to predict microstructure and hardness on L-PBF built high-strength, low-alloy steel parts. A moving heat-source model was used in this analysis to accurately predict the temperature history. A kinetics based model which was developed to predict microstructure in the heat-affected zone of a welded joint was extended to predict the microstructure and hardness in an L-PBF build by inputting the predicted temperature history. The tempering effect resulting from the following built layers on the current-layer microstructural phases were modeled, which is the key to predict the final hardness correctly. It was also found that the top layers of a build part have higher hardness because of the lack of the tempering effect. A sequentially coupled thermal and mechanical analysis method was developed to predict residual stress and deformation for an L-PBF build part. It was found that a line-heating model is not suitable for analyzing a large L-PBF built part. The layer heating method is a potential method for analyzing a large L-PBF built part. The experiment was conducted to validate the model predictions.

Use of empirical likelihood to calibrate auxiliary information in partly linear monotone regression models.

PubMed

Chen, Baojiang; Qin, Jing

2014-05-10

In statistical analysis, a regression model is needed if one is interested in finding the relationship between a response variable and covariates. When the response depends on the covariate, then it may also depend on the function of this covariate. If one has no knowledge of this functional form but expect for monotonic increasing or decreasing, then the isotonic regression model is preferable. Estimation of parameters for isotonic regression models is based on the pool-adjacent-violators algorithm (PAVA), where the monotonicity constraints are built in. With missing data, people often employ the augmented estimating method to improve estimation efficiency by incorporating auxiliary information through a working regression model. However, under the framework of the isotonic regression model, the PAVA does not work as the monotonicity constraints are violated. In this paper, we develop an empirical likelihood-based method for isotonic regression model to incorporate the auxiliary information. Because the monotonicity constraints still hold, the PAVA can be used for parameter estimation. Simulation studies demonstrate that the proposed method can yield more efficient estimates, and in some situations, the efficiency improvement is substantial. We apply this method to a dementia study. Copyright © 2013 John Wiley & Sons, Ltd.

Performance of circuit switching in the Internet

NASA Astrophysics Data System (ADS)

Molinero-Fernández, Pablo; McKeown, Nick

2003-04-01

We study the performance of an Internet that uses circuit switching (CS) instead of, or in addition to, packet switching (PS). On the face of it, this would seem a pointless exercise; the Internet is packet switched, and it was deliberately built that way to enable the efficiencies afforded by statistical multiplexing and the robustness of fast rerouting around failures. But link utilization is low particularly at the core of the Internet, which makes statistical multiplexing less important than it once was. Moreover, circuit switches today are capable of rapid reconfiguration around failures. There is also renewed interest in CS because of the ease of building very-high-capacity optical circuit switches. Although several proposals have suggested ways in which CS may be introduced into the Internet, the research presented here is based on Transmission Control Protocol (TCP) switching, in which a new circuit is created for each application flow. Here we explore the performance of a network that uses TCP switching, with particular emphasis on the response time experienced by users. We use simple M/GI/1 and M/GI/N queues to model application flows in both packet-switched and circuit-switched networks, as well as ns-2 simulations. We conclude that because of high-bandwidth long-lived flows, it does not make sense to use CS in shared-access or local area networks. But our results suggest that in the core of the network, where high capacity is needed most, and where peak flow rate is limited by the access link, there is little or no difference in performance between CS and PS. Given that circuit switches can be built to be much faster than packet switches, this suggests that a circuit-switched core warrants further investigation.

Building mental models by dissecting physical models.

PubMed

Srivastava, Anveshna

2016-01-01

When students build physical models from prefabricated components to learn about model systems, there is an implicit trade-off between the physical degrees of freedom in building the model and the intensity of instructor supervision needed. Models that are too flexible, permitting multiple possible constructions require greater supervision to ensure focused learning; models that are too constrained require less supervision, but can be constructed mechanically, with little to no conceptual engagement. We propose "model-dissection" as an alternative to "model-building," whereby instructors could make efficient use of supervisory resources, while simultaneously promoting focused learning. We report empirical results from a study conducted with biology undergraduate students, where we demonstrate that asking them to "dissect" out specific conceptual structures from an already built 3D physical model leads to a significant improvement in performance than asking them to build the 3D model from simpler components. Using questionnaires to measure understanding both before and after model-based interventions for two cohorts of students, we find that both the "builders" and the "dissectors" improve in the post-test, but it is the latter group who show statistically significant improvement. These results, in addition to the intrinsic time-efficiency of "model dissection," suggest that it could be a valuable pedagogical tool. © 2015 The International Union of Biochemistry and Molecular Biology.

Upscaling: Effective Medium Theory, Numerical Methods and the Fractal Dream

NASA Astrophysics Data System (ADS)

Guéguen, Y.; Ravalec, M. Le; Ricard, L.

2006-06-01

Upscaling is a major issue regarding mechanical and transport properties of rocks. This paper examines three issues relative to upscaling. The first one is a brief overview of Effective Medium Theory (EMT), which is a key tool to predict average rock properties at a macroscopic scale in the case of a statistically homogeneous medium. EMT is of particular interest in the calculation of elastic properties. As discussed in this paper, EMT can thus provide a possible way to perform upscaling, although it is by no means the only one, and in particular it is irrelevant if the medium does not adhere to statistical homogeneity. This last circumstance is examined in part two of the paper. We focus on the example of constructing a hydrocarbon reservoir model. Such a construction is a required step in the process of making reasonable predictions for oil production. Taking into account rock permeability, lithological units and various structural discontinuities at different scales is part of this construction. The result is that stochastic reservoir models are built that rely on various numerical upscaling methods. These methods are reviewed. They provide techniques which make it possible to deal with upscaling on a general basis. Finally, a last case in which upscaling is trivial is considered in the third part of the paper. This is the fractal case. Fractal models have become popular precisely because they are free of the assumption of statistical homogeneity and yet do not involve numerical methods. It is suggested that using a physical criterion as a means to discriminate whether fractality is a dream or reality would be more satisfactory than relying on a limited data set alone.

School Libraries and Science Achievement: A View from Michigan's Middle Schools

ERIC Educational Resources Information Center

Mardis, Marcia

2007-01-01

If strong school library media centers (SLMCs) positively impact middle school student reading achievement, as measured on standardized tests, are they also beneficial for middle school science achievement? To answer this question, the researcher built upon the statistical analyses used in previous school library impact studies with qualitative…

Statistical Study to Evaluate the Effect of Processing Variables on Shrinkage Incidence During Solidification of Nodular Cast Irons

NASA Astrophysics Data System (ADS)

Gutiérrez, J. M.; Natxiondo, A.; Nieves, J.; Zabala, A.; Sertucha, J.

2017-04-01

The study of shrinkage incidence variations in nodular cast irons is an important aspect of manufacturing processes. These variations change the feeding requirements on castings and the optimization of risers' size is consequently affected when avoiding the formation of shrinkage defects. The effect of a number of processing variables on the shrinkage size has been studied using a layout specifically designed for this purpose. The β parameter has been defined as the relative volume reduction from the pouring temperature up to the room temperature. It is observed that shrinkage size and β decrease as effective carbon content increases and when inoculant is added in the pouring stream. A similar effect is found when the parameters selected from cooling curves show high graphite nucleation during solidification of cast irons for a given inoculation level. Pearson statistical analysis has been used to analyze the correlations among all involved variables and a group of Bayesian networks have been subsequently built so as to get the best accurate model for predicting β as a function of the input processing variables. The developed models can be used in foundry plants to study the shrinkage incidence variations in the manufacturing process and to optimize the related costs.

A study of two unsupervised data driven statistical methodologies for detecting and classifying damages in structural health monitoring

NASA Astrophysics Data System (ADS)

Tibaduiza, D.-A.; Torres-Arredondo, M.-A.; Mujica, L. E.; Rodellar, J.; Fritzen, C.-P.

2013-12-01

This article is concerned with the practical use of Multiway Principal Component Analysis (MPCA), Discrete Wavelet Transform (DWT), Squared Prediction Error (SPE) measures and Self-Organizing Maps (SOM) to detect and classify damages in mechanical structures. The formalism is based on a distributed piezoelectric active sensor network for the excitation and detection of structural dynamic responses. Statistical models are built using PCA when the structure is known to be healthy either directly from the dynamic responses or from wavelet coefficients at different scales representing Time-frequency information. Different damages on the tested structures are simulated by adding masses at different positions. The data from the structure in different states (damaged or not) are then projected into the different principal component models by each actuator in order to obtain the input feature vectors for a SOM from the scores and the SPE measures. An aircraft fuselage from an Airbus A320 and a multi-layered carbon fiber reinforced plastic (CFRP) plate are used as examples to test the approaches. Results are presented, compared and discussed in order to determine their potential in structural health monitoring. These results showed that all the simulated damages were detectable and the selected features proved capable of separating all damage conditions from the undamaged state for both approaches.

Additive interaction between heterogeneous environmental ...

EPA Pesticide Factsheets

BACKGROUND Environmental exposures often occur in tandem; however, epidemiological research often focuses on singular exposures. Statistical interactions among broad, well-characterized environmental domains have not yet been evaluated in association with health. We address this gap by conducting a county-level cross-sectional analysis of interactions between Environmental Quality Index (EQI) domain indices on preterm birth in the Unites States from 2000-2005.METHODS: The EQI, a county-level index constructed for the 2000-2005 time period, was constructed from five domain-specific indices (air, water, land, built and sociodemographic) using principal component analyses. County-level preterm birth rates (n=3141) were estimated using live births from the National Center for Health Statistics. Linear regression was used to estimate prevalence differences (PD) and 95% confidence intervals (CI) comparing worse environmental quality to the better quality for each model for a) each individual domain main effect b) the interaction contrast and c) the two main effects plus interaction effect (i.e. the “net effect”) to show departure from additive interaction for the all U.S counties. Analyses were also performed for subgroupings by four urban/rural strata. RESULTS: We found the suggestion of antagonistic interactions but no synergism, along with several purely additive (i.e., no interaction) associations. In the non-stratified model, we observed antagonistic interac

Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control.

PubMed

Adeleke, Jude Adekunle; Moodley, Deshendran; Rens, Gavin; Adewumi, Aderemi Oluyinka

2017-04-09

Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM 2 . 5 pollution situations is integrated into the proactive monitoring and control framework. Results show that the proposed approach can effectively predict short term PM 2 . 5 pollution situations: precision of up to 0.86 and sensitivity of up to 0.85 is achieved over half hour prediction horizons, making it possible for the system to warn occupants or even to autonomously avert the predicted pollution situations within the context of Semantic Sensor Web.

Modeling Age-Friendly Environment, Active Aging, and Social Connectedness in an Emerging Asian Economy.

PubMed

Lai, Ming-Ming; Lein, Shi-Ying; Lau, Siok-Hwa; Lai, Ming-Ling

2016-01-01

This paper empirically tested eight key features of WHO guidelines to age-friendly community by surveying 211 informal caregivers and 402 self-care adults (aged 45 to 85 and above) in Malaysia. We examined the associations of these eight features with active aging and social connectedness through exploratory and confirmatory factor analyses. A structural model with satisfactory goodness-of-fit indices (CMIN/df = 1.11, RMSEA = 0.02, NFI = 0.97, TLI = 1.00, CFI = 1.00, and GFI = 0.96) indicates that transportation and housing, community support and health services, and outdoor spaces and buildings are statistically significant in creating an age-friendly environment. We found a statistically significant positive relationship between an age-friendly environment and active aging. This relationship is mediated by social connectedness. The results indicate that built environments such as accessible public transportations and housing, affordable and accessible healthcare services, and elderly friendly outdoor spaces and buildings have to be put into place before social environment in building an age-friendly environment. Otherwise, the structural barriers would hinder social interactions for the aged. The removal of the environmental barriers and improved public transportation services provide short-term solutions to meet the varied and growing needs of the older population.

Modeling Age-Friendly Environment, Active Aging, and Social Connectedness in an Emerging Asian Economy

PubMed Central

Lai, Ming-Ming; Lein, Shi-Ying; Lau, Siok-Hwa; Lai, Ming-Ling

2016-01-01

This paper empirically tested eight key features of WHO guidelines to age-friendly community by surveying 211 informal caregivers and 402 self-care adults (aged 45 to 85 and above) in Malaysia. We examined the associations of these eight features with active aging and social connectedness through exploratory and confirmatory factor analyses. A structural model with satisfactory goodness-of-fit indices (CMIN/df = 1.11, RMSEA = 0.02, NFI = 0.97, TLI = 1.00, CFI = 1.00, and GFI = 0.96) indicates that transportation and housing, community support and health services, and outdoor spaces and buildings are statistically significant in creating an age-friendly environment. We found a statistically significant positive relationship between an age-friendly environment and active aging. This relationship is mediated by social connectedness. The results indicate that built environments such as accessible public transportations and housing, affordable and accessible healthcare services, and elderly friendly outdoor spaces and buildings have to be put into place before social environment in building an age-friendly environment. Otherwise, the structural barriers would hinder social interactions for the aged. The removal of the environmental barriers and improved public transportation services provide short-term solutions to meet the varied and growing needs of the older population. PMID:27293889

Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control

PubMed Central

Adeleke, Jude Adekunle; Moodley, Deshendran; Rens, Gavin; Adewumi, Aderemi Oluyinka

2017-01-01

Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM2.5 pollution situations is integrated into the proactive monitoring and control framework. Results show that the proposed approach can effectively predict short term PM2.5 pollution situations: precision of up to 0.86 and sensitivity of up to 0.85 is achieved over half hour prediction horizons, making it possible for the system to warn occupants or even to autonomously avert the predicted pollution situations within the context of Semantic Sensor Web. PMID:28397776

Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy.

PubMed

Fang, Jiansong; Wu, Zengrui; Cai, Chuipu; Wang, Qi; Tang, Yun; Cheng, Feixiong

2017-11-27

Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug-target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure-drug-target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein, and kaempferol) with high scores were validated by various literature studies. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug-target interactions of natural products with known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram, and metformin) with new mechanism-of-action were validated by various published experimental evidence. In summary, this study offers powerful computational systems pharmacology approaches and tools for the development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.

Quality-by-Design approach to monitor the operation of a batch bioreactor in an industrial avian vaccine manufacturing process.

PubMed

Largoni, Martina; Facco, Pierantonio; Bernini, Donatella; Bezzo, Fabrizio; Barolo, Massimiliano

2015-10-10

Monitoring batch bioreactors is a complex task, due to the fact that several sources of variability can affect a running batch and impact on the final product quality. Additionally, the product quality itself may not be measurable on line, but requires sampling and lab analysis taking several days to be completed. In this study we show that, by using appropriate process analytical technology tools, the operation of an industrial batch bioreactor used in avian vaccine manufacturing can be effectively monitored as the batch progresses. Multivariate statistical models are built from historical databases of batches already completed, and they are used to enable the real time identification of the variability sources, to reliably predict the final product quality, and to improve process understanding, paving the way to a reduction of final product rejections, as well as to a reduction of the product cycle time. It is also shown that the product quality "builds up" mainly during the first half of a batch, suggesting on the one side that reducing the variability during this period is crucial, and on the other side that the batch length can possibly be shortened. Overall, the study demonstrates that, by using a Quality-by-Design approach centered on the appropriate use of mathematical modeling, quality can indeed be built "by design" into the final product, whereas the role of end-point product testing can progressively reduce its importance in product manufacturing. Copyright © 2015 Elsevier B.V. All rights reserved.

Statistical Downscaling Of Local Climate In The Alpine Region

NASA Astrophysics Data System (ADS)

Kaspar, Severin; Philipp, Andreas; Jacobeit, Jucundus

2016-04-01

The impact of climate change on the alpine region was disproportional strong in the past decades compared to the surrounding areas, which becomes manifest in a higher increase in surface air temperature. Beside the thermal changes also implications for the hydrological cycle may be expected, acting as a very important factor not only for the ecosystem but also for mankind, in the form of water security or considering economical aspects like winter tourism etc. Therefore, in climate impact studies, it is necessary to focus on variables with high influence on the hydrological cycle, for example temperature, precipitation, wind, humidity and radiation. The aim of this study is to build statistical downscaling models which are able to reproduce temperature and precipitation at the mountainous alpine weather stations Zugspitze and Sonnblick and to further project these models into the future to identify possible changes in the behavior of these climate variables and with that in the hydrological cycle. Beside facing a in general very complex terrain in this high elevated regions, we have the advantage of a more direct atmospheric influence on the meteorology of the exposed weather stations from the large scale circulation. Two nonlinear statistical methods are developed to model the station-data series on a daily basis: On the one hand a conditional classification approach was used and on the other hand a model based on artificial neural networks (ANNs) was built. The latter is in focus of this presentation. One of the important steps of developing a new model approach is to find a reliable predictor setup with e.g. informative predictor variables or adequate location and size of the spatial domain. The question is: Can we include synoptic background knowledge to identify an optimal domain for an ANN approach? The yet developed ANN setups and configurations show promising results in downscaling both, temperature (up to 80 % of explained variance) and precipitation (up to 60 % of explained variance).

The road map towards providing a robust Raman spectroscopy-based cancer diagnostic platform and integration into clinic

NASA Astrophysics Data System (ADS)

Lau, Katherine; Isabelle, Martin; Lloyd, Gavin R.; Old, Oliver; Shepherd, Neil; Bell, Ian M.; Dorney, Jennifer; Lewis, Aaran; Gaifulina, Riana; Rodriguez-Justo, Manuel; Kendall, Catherine; Stone, Nicolas; Thomas, Geraint; Reece, David

2016-03-01

Despite the demonstrated potential as an accurate cancer diagnostic tool, Raman spectroscopy (RS) is yet to be adopted by the clinic for histopathology reviews. The Stratified Medicine through Advanced Raman Technologies (SMART) consortium has begun to address some of the hurdles in its adoption for cancer diagnosis. These hurdles include awareness and acceptance of the technology, practicality of integration into the histopathology workflow, data reproducibility and availability of transferrable models. We have formed a consortium, in joint efforts, to develop optimised protocols for tissue sample preparation, data collection and analysis. These protocols will be supported by provision of suitable hardware and software tools to allow statistically sound classification models to be built and transferred for use on different systems. In addition, we are building a validated gastrointestinal (GI) cancers model, which can be trialled as part of the histopathology workflow at hospitals, and a classification tool. At the end of the project, we aim to deliver a robust Raman based diagnostic platform to enable clinical researchers to stage cancer, define tumour margin, build cancer diagnostic models and discover novel disease bio markers.

Numerical Model of the Hoosic River Flood-Control Channel, Adams, MA

DTIC Science & Technology

2010-02-01

The model was then used to evaluate the flow conditions associated with the as-built channel configuration. The existing channel conditions were then...end as part of a channel restoration project. The model was to determine if restoration alterations would change water- surface elevations associated ...water-surface elevations associated with the initial design and construction. After as-built flow conditions were established, flow conditions

Estimating extent of mortality associated with the Douglas-fir beetle in the Central and Northern Rockies

Treesearch

Jose F. Negron; Willis C. Schaupp; Kenneth E. Gibson; John Anhold; Dawn Hansen; Ralph Thier; Phil Mocettini

1999-01-01

Data collected from Douglas-fir stands infected by the Douglas-fir beetle in Wyoming, Montana, Idaho, and Utah, were used to develop models to estimate amount of mortality in terms of basal area killed. Models were built using stepwise linear regression and regression tree approaches. Linear regression models using initial Douglas-fir basal area were built for all...

Time-series panel analysis (TSPA): multivariate modeling of temporal associations in psychotherapy process.

PubMed

Ramseyer, Fabian; Kupper, Zeno; Caspar, Franz; Znoj, Hansjörg; Tschacher, Wolfgang

2014-10-01

Processes occurring in the course of psychotherapy are characterized by the simple fact that they unfold in time and that the multiple factors engaged in change processes vary highly between individuals (idiographic phenomena). Previous research, however, has neglected the temporal perspective by its traditional focus on static phenomena, which were mainly assessed at the group level (nomothetic phenomena). To support a temporal approach, the authors introduce time-series panel analysis (TSPA), a statistical methodology explicitly focusing on the quantification of temporal, session-to-session aspects of change in psychotherapy. TSPA-models are initially built at the level of individuals and are subsequently aggregated at the group level, thus allowing the exploration of prototypical models. TSPA is based on vector auto-regression (VAR), an extension of univariate auto-regression models to multivariate time-series data. The application of TSPA is demonstrated in a sample of 87 outpatient psychotherapy patients who were monitored by postsession questionnaires. Prototypical mechanisms of change were derived from the aggregation of individual multivariate models of psychotherapy process. In a 2nd step, the associations between mechanisms of change (TSPA) and pre- to postsymptom change were explored. TSPA allowed a prototypical process pattern to be identified, where patient's alliance and self-efficacy were linked by a temporal feedback-loop. Furthermore, therapist's stability over time in both mastery and clarification interventions was positively associated with better outcomes. TSPA is a statistical tool that sheds new light on temporal mechanisms of change. Through this approach, clinicians may gain insight into prototypical patterns of change in psychotherapy. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Automated planning of ablation targets in atrial fibrillation treatment

NASA Astrophysics Data System (ADS)

Keustermans, Johannes; De Buck, Stijn; Heidbüchel, Hein; Suetens, Paul

2011-03-01

Catheter based radio-frequency ablation is used as an invasive treatment of atrial fibrillation. This procedure is often guided by the use of 3D anatomical models obtained from CT, MRI or rotational angiography. During the intervention the operator accurately guides the catheter to prespecified target ablation lines. The planning stage, however, can be time consuming and operator dependent which is suboptimal both from a cost and health perspective. Therefore, we present a novel statistical model-based algorithm for locating ablation targets from 3D rotational angiography images. Based on a training data set of 20 patients, consisting of 3D rotational angiography images with 30 manually indicated ablation points, a statistical local appearance and shape model is built. The local appearance model is based on local image descriptors to capture the intensity patterns around each ablation point. The local shape model is constructed by embedding the ablation points in an undirected graph and imposing that each ablation point only interacts with its neighbors. Identifying the ablation points on a new 3D rotational angiography image is performed by proposing a set of possible candidate locations for each ablation point, as such, converting the problem into a labeling problem. The algorithm is validated using a leave-one-out-approach on the training data set, by computing the distance between the ablation lines obtained by the algorithm and the manually identified ablation points. The distance error is equal to 3.8+/-2.9 mm. As ablation lesion size is around 5-7 mm, automated planning of ablation targets by the presented approach is sufficiently accurate.

Understanding Physical Activity through Interactions Between the Built Environment and Social Cognition: A Systematic Review.

PubMed

Rhodes, Ryan E; Saelens, Brian E; Sauvage-Mar, Claire

2018-05-16

Few people in most developed nations engage in regular physical activity (PA), despite its well-established health benefits. Socioecological models highlight the potential interaction of multiple factors from policy and the built environment to individual social cognition in explaining PA. The purpose of this review was to appraise this interaction tenet of the socioecological model between the built environment and social cognition to predict PA. Eligible studies had to have been published in peer-reviewed journals in the English language, and included any tests of interaction between social cognition and the built environment with PA. Literature searches, concluded in October 2017, used five common databases. Findings were grouped by type of PA outcomes (leisure, transportation, total PA and total moderate-vigorous PA [MVPA]), then grouped by the type of interactions between social cognitive and built environment constructs. The initial search yielded 308 hits, which was reduced to 22 independent studies of primarily high- to medium-quality after screening for eligibility criteria. The interaction tenet of the socioecological model was not supported for overall MVPA and total PA. By contrast, while there was heterogeneity of findings for leisure-time PA, environmental accessibility/convenience interacted with intention, and environmental aesthetics interacted with affective judgments, to predict leisure-time PA. Interactions between the built environment and social cognition in PA for transport are limited, with current results failing to support an effect. The results provide some support for interactive aspects of the built environment and social cognition in leisure-time PA, and thus highlight potential areas for integrated intervention of individual and environmental change.

A probabilistic method for streamflow projection and associated uncertainty analysis in a data sparse alpine region

NASA Astrophysics Data System (ADS)

Ren, Weiwei; Yang, Tao; Shi, Pengfei; Xu, Chong-yu; Zhang, Ke; Zhou, Xudong; Shao, Quanxi; Ciais, Philippe

2018-06-01

Climate change imposes profound influence on regional hydrological cycle and water security in many alpine regions worldwide. Investigating regional climate impacts using watershed scale hydrological models requires a large number of input data such as topography, meteorological and hydrological data. However, data scarcity in alpine regions seriously restricts evaluation of climate change impacts on water cycle using conventional approaches based on global or regional climate models, statistical downscaling methods and hydrological models. Therefore, this study is dedicated to development of a probabilistic model to replace the conventional approaches for streamflow projection. The probabilistic model was built upon an advanced Bayesian Neural Network (BNN) approach directly fed by the large-scale climate predictor variables and tested in a typical data sparse alpine region, the Kaidu River basin in Central Asia. Results show that BNN model performs better than the general methods across a number of statistical measures. The BNN method with flexible model structures by active indicator functions, which reduce the dependence on the initial specification for the input variables and the number of hidden units, can work well in a data limited region. Moreover, it can provide more reliable streamflow projections with a robust generalization ability. Forced by the latest bias-corrected GCM scenarios, streamflow projections for the 21st century under three RCP emission pathways were constructed and analyzed. Briefly, the proposed probabilistic projection approach could improve runoff predictive ability over conventional methods and provide better support to water resources planning and management under data limited conditions as well as enable a facilitated climate change impact analysis on runoff and water resources in alpine regions worldwide.

Maximum Power Point Tracking Control of Hydrokinetic Turbine and Low-speed High-Thrust Permanent Magnet Generator Design

DTIC Science & Technology

2012-01-01

Schematic of the generator and power converters built in PLECS ............. 26 Figure 3.12. Block diagram of the MPPT control built in Matlab/Simulink...validated by simulation results done in Matlab/Simulink R2010a and PLECS . Figure 3.9 shows the block diagram of the hydrokinetic system built in Matlab...rectifier, boost converter and battery model built in PLECS . The battery bank on the load side is simulated by a constant dc voltage source. 25

An Interactive Multimedia Learning Environment for VLSI Built with COSMOS

ERIC Educational Resources Information Center

Angelides, Marios C.; Agius, Harry W.

2002-01-01

This paper presents Bigger Bits, an interactive multimedia learning environment that teaches students about VLSI within the context of computer electronics. The system was built with COSMOS (Content Oriented semantic Modelling Overlay Scheme), which is a modelling scheme that we developed for enabling the semantic content of multimedia to be used…

Effect of fertility on secondary sex ratio and twinning rate in Sweden, 1749-1870.

PubMed

Fellman, Johan; Eriksson, Aldur W

2015-02-01

We analyzed the effect of total fertility rate (TFR) and crude birth rate (CBR) on the number of males per 100 females at birth, also called the secondary sex ratio (SR), and on the twinning rate (TWR). Earlier studies have noted regional variations in TWR and racial differences in the SR. Statistical analyses have shown that comparisons between SRs demand large data sets because random fluctuations in moderate data are marked. Consequently, reliable results presuppose national birth data. Here, we analyzed historical demographic data and their regional variations between counties in Sweden. We built spatial models for the TFR in 1860 and the CBR in 1751-1870, and as regressors we used geographical coordinates for the provincial capitals of the counties. For both variables, we obtained significant spatial variations, albeit of different patterns and power. The SR among the live-born in 1749-1869 and the TWR in 1751-1860 showed slight spatial variations. The influence of CBR and TFR on the SR and TWR was examined and statistical significant effects were found.

Flood probability quantification for road infrastructure: Data-driven spatial-statistical approach and case study applications.

PubMed

Kalantari, Zahra; Cavalli, Marco; Cantone, Carolina; Crema, Stefano; Destouni, Georgia

2017-03-01

Climate-driven increase in the frequency of extreme hydrological events is expected to impose greater strain on the built environment and major transport infrastructure, such as roads and railways. This study develops a data-driven spatial-statistical approach to quantifying and mapping the probability of flooding at critical road-stream intersection locations, where water flow and sediment transport may accumulate and cause serious road damage. The approach is based on novel integration of key watershed and road characteristics, including also measures of sediment connectivity. The approach is concretely applied to and quantified for two specific study case examples in southwest Sweden, with documented road flooding effects of recorded extreme rainfall. The novel contributions of this study in combining a sediment connectivity account with that of soil type, land use, spatial precipitation-runoff variability and road drainage in catchments, and in extending the connectivity measure use for different types of catchments, improve the accuracy of model results for road flood probability. Copyright © 2016 Elsevier B.V. All rights reserved.

GeoSegmenter: A statistically learned Chinese word segmenter for the geoscience domain

NASA Astrophysics Data System (ADS)

Huang, Lan; Du, Youfu; Chen, Gongyang

2015-03-01

Unlike English, the Chinese language has no space between words. Segmenting texts into words, known as the Chinese word segmentation (CWS) problem, thus becomes a fundamental issue for processing Chinese documents and the first step in many text mining applications, including information retrieval, machine translation and knowledge acquisition. However, for the geoscience subject domain, the CWS problem remains unsolved. Although a generic segmenter can be applied to process geoscience documents, they lack the domain specific knowledge and consequently their segmentation accuracy drops dramatically. This motivated us to develop a segmenter specifically for the geoscience subject domain: the GeoSegmenter. We first proposed a generic two-step framework for domain specific CWS. Following this framework, we built GeoSegmenter using conditional random fields, a principled statistical framework for sequence learning. Specifically, GeoSegmenter first identifies general terms by using a generic baseline segmenter. Then it recognises geoscience terms by learning and applying a model that can transform the initial segmentation into the goal segmentation. Empirical experimental results on geoscience documents and benchmark datasets showed that GeoSegmenter could effectively recognise both geoscience terms and general terms.

Identifying and characterizing hepatitis C virus hotspots in Massachusetts: a spatial epidemiological approach.

PubMed

Stopka, Thomas J; Goulart, Michael A; Meyers, David J; Hutcheson, Marga; Barton, Kerri; Onofrey, Shauna; Church, Daniel; Donahue, Ashley; Chui, Kenneth K H

2017-04-20

Hepatitis C virus (HCV) infections have increased during the past decade but little is known about geographic clustering patterns. We used a unique analytical approach, combining geographic information systems (GIS), spatial epidemiology, and statistical modeling to identify and characterize HCV hotspots, statistically significant clusters of census tracts with elevated HCV counts and rates. We compiled sociodemographic and HCV surveillance data (n = 99,780 cases) for Massachusetts census tracts (n = 1464) from 2002 to 2013. We used a five-step spatial epidemiological approach, calculating incremental spatial autocorrelations and Getis-Ord Gi* statistics to identify clusters. We conducted logistic regression analyses to determine factors associated with the HCV hotspots. We identified nine HCV clusters, with the largest in Boston, New Bedford/Fall River, Worcester, and Springfield (p < 0.05). In multivariable analyses, we found that HCV hotspots were independently and positively associated with the percent of the population that was Hispanic (adjusted odds ratio [AOR]: 1.07; 95% confidence interval [CI]: 1.04, 1.09) and the percent of households receiving food stamps (AOR: 1.83; 95% CI: 1.22, 2.74). HCV hotspots were independently and negatively associated with the percent of the population that were high school graduates or higher (AOR: 0.91; 95% CI: 0.89, 0.93) and the percent of the population in the "other" race/ethnicity category (AOR: 0.88; 95% CI: 0.85, 0.91). We identified locations where HCV clusters were a concern, and where enhanced HCV prevention, treatment, and care can help combat the HCV epidemic in Massachusetts. GIS, spatial epidemiological and statistical analyses provided a rigorous approach to identify hotspot clusters of disease, which can inform public health policy and intervention targeting. Further studies that incorporate spatiotemporal cluster analyses, Bayesian spatial and geostatistical models, spatially weighted regression analyses, and assessment of associations between HCV clustering and the built environment are needed to expand upon our combined spatial epidemiological and statistical methods.

A stochastic flow-capturing model to optimize the location of fast-charging stations with uncertain electric vehicle flows

DOE PAGES

Wu, Fei; Sioshansi, Ramteen

2017-05-04

Here, we develop a model to optimize the location of public fast charging stations for electric vehicles (EVs). A difficulty in planning the placement of charging stations is uncertainty in where EV charging demands appear. For this reason, we use a stochastic flow-capturing location model (SFCLM). A sample-average approximation method and an averaged two-replication procedure are used to solve the problem and estimate the solution quality. We demonstrate the use of the SFCLM using a Central-Ohio based case study. We find that most of the stations built are concentrated around the urban core of the region. As the number ofmore » stations built increases, some appear on the outskirts of the region to provide an extended charging network. We find that the sets of optimal charging station locations as a function of the number of stations built are approximately nested. We demonstrate the benefits of the charging-station network in terms of how many EVs are able to complete their daily trips by charging midday—six public charging stations allow at least 60% of EVs that would otherwise not be able to complete their daily tours without the stations to do so. We finally compare the SFCLM to a deterministic model, in which EV flows are set equal to their expected values. We show that if a limited number of charging stations are to be built, the SFCLM outperforms the deterministic model. As the number of stations to be built increases, the SFCLM and deterministic model select very similar station locations.« less

Forward Modeling of Large-scale Structure: An Open-source Approach with Halotools

DOE PAGES

Hearin, Andrew P.; Campbell, Duncan; Tollerud, Erik; ...

2017-10-20

Here, we present the first stable release of Halotools (v0.2), a community-driven Python package designed to build and test models of the galaxy-halo connection. Halotools provides a modular platform for creating mock universes of galaxies starting from a catalog of dark matter halos obtained from a cosmological simulation. The package supports many of the common forms used to describe galaxy-halo models: the halo occupation distribution (HOD), the conditional luminosity function (CLF), abundance matching, and alternatives to these models that include effects such as environmental quenching or variable galaxy assembly bias. Satellite galaxies can be modeled to live in subhalos, ormore » to follow custom number density profiles within their halos, including spatial and/or velocity bias with respect to the dark matter profile. Here, the package has an optimized toolkit to make mock observations on a synthetic galaxy population, including galaxy clustering, galaxy-galaxy lensing, galaxy group identification, RSD multipoles, void statistics, pairwise velocities and others, allowing direct comparison to observations. Halotools is object-oriented, enabling complex models to be built from a set of simple, interchangeable components, including those of your own creation.« less

Image reconstruction and system modeling techniques for virtual-pinhole PET insert systems

PubMed Central

Keesing, Daniel B; Mathews, Aswin; Komarov, Sergey; Wu, Heyu; Song, Tae Yong; O'Sullivan, Joseph A; Tai, Yuan-Chuan

2012-01-01

Virtual-pinhole PET (VP-PET) imaging is a new technology in which one or more high-resolution detector modules are integrated into a conventional PET scanner with lower-resolution detectors. It can locally enhance the spatial resolution and contrast recovery near the add-on detectors, and depending on the configuration, may also increase the sensitivity of the system. This novel scanner geometry makes the reconstruction problem more challenging compared to the reconstruction of data from a standalone PET scanner, as new techniques are needed to model and account for the non-standard acquisition. In this paper, we present a general framework for fully 3D modeling of an arbitrary VP-PET insert system. The model components are incorporated into a statistical reconstruction algorithm to estimate an image from the multi-resolution data. For validation, we apply the proposed model and reconstruction approach to one of our custom-built VP-PET systems – a half-ring insert device integrated into a clinical PET/CT scanner. Details regarding the most important implementation issues are provided. We show that the proposed data model is consistent with the measured data, and that our approach can lead to reconstructions with improved spatial resolution and lesion detectability. PMID:22490983

Forward Modeling of Large-scale Structure: An Open-source Approach with Halotools

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hearin, Andrew P.; Campbell, Duncan; Tollerud, Erik

Here, we present the first stable release of Halotools (v0.2), a community-driven Python package designed to build and test models of the galaxy-halo connection. Halotools provides a modular platform for creating mock universes of galaxies starting from a catalog of dark matter halos obtained from a cosmological simulation. The package supports many of the common forms used to describe galaxy-halo models: the halo occupation distribution (HOD), the conditional luminosity function (CLF), abundance matching, and alternatives to these models that include effects such as environmental quenching or variable galaxy assembly bias. Satellite galaxies can be modeled to live in subhalos, ormore » to follow custom number density profiles within their halos, including spatial and/or velocity bias with respect to the dark matter profile. Here, the package has an optimized toolkit to make mock observations on a synthetic galaxy population, including galaxy clustering, galaxy-galaxy lensing, galaxy group identification, RSD multipoles, void statistics, pairwise velocities and others, allowing direct comparison to observations. Halotools is object-oriented, enabling complex models to be built from a set of simple, interchangeable components, including those of your own creation.« less

Three-Dimensional City Determinants of the Urban Heat Island: A Statistical Approach

NASA Astrophysics Data System (ADS)

Chun, Bum Seok

There is no doubt that the Urban Heat Island (UHI) is a mounting problem in built-up environments, due to the energy retention by the surface materials of dense buildings, leading to increased temperatures, air pollution, and energy consumption. Much of the earlier research on the UHI has used two-dimensional (2-D) information, such as land uses and the distribution of vegetation. In the case of homogeneous land uses, it is possible to predict surface temperatures with reasonable accuracy with 2-D information. However, three-dimensional (3-D) information is necessary to analyze more complex sites, including dense building clusters. Recent research on the UHI has started to consider multi-dimensional models. The purpose of this research is to explore the urban determinants of the UHI, using 2-D/3-D urban information with statistical modeling. The research includes the following stages: (a) estimating urban temperature, using satellite images, (b) developing a 3-D city model by LiDAR data, (c) generating geometric parameters with regard to 2-/3-D geospatial information, and (d) conducting different statistical analyses: OLS and spatial regressions. The research area is part of the City of Columbus, Ohio. To effectively and systematically analyze the UHI, hierarchical grid scales (480m, 240m, 120m, 60m, and 30m) are proposed, together with linear and the log-linear regression models. The non-linear OLS models with Log(AST) as dependent variable have the highest R2 among all the OLS-estimated models. However, both SAR and GSM models are estimated for the 480m, 240m, 120m, and 60m grids to reduce their spatial dependency. Most GSM models have R2s higher than 0.9, except for the 240m grid. Overall, the urban characteristics having high impacts in all grids are embodied in solar radiation, 3-D open space, greenery, and water streams. These results demonstrate that it is possible to mitigate the UHI, providing guidelines for policies aiming to reduce the UHI.

Categorisation of built environment characteristics: the trouble with tertiles.

PubMed

Lamb, Karen E; White, Simon R

2015-02-15

In the analysis of the effect of built environment features on health, it is common for researchers to categorise built environment exposure variables based on arbitrary percentile cut-points, such as median or tertile splits. This arbitrary categorisation leads to a loss of information and a lack of comparability between studies since the choice of cut-point is based on the sample distribution. In this paper, we highlight the various drawbacks of adopting percentile categorisation of exposure variables. Using data from the SocioEconomic Status and Activity in Women (SESAW) study from Melbourne, Australia, we highlight alternative approaches which may be used instead of percentile categorisation in order to assess built environment effects on health. We discuss these approaches using an example which examines the association between the number of accessible supermarkets and body mass index. We show that alternative approaches to percentile categorisation, such as transformations of the exposure variable or factorial polynomials, can be implemented easily using standard statistical software packages. These procedures utilise all of the available information available in the data, avoiding a loss of power as experienced when categorisation is adopted.We argue that researchers should retain all available information by using the continuous exposure, adopting transformations where necessary.

Validating Stormwater system simulations in Edmonton Using MIKE URBAN

NASA Astrophysics Data System (ADS)

Gaafar, M.

2016-12-01

Many municipalities use chloramination to disinfect drinking water so as to avert the production of the disinfection by-products (DBPs) that result from conventional chlorination processes and the consequential public health risks. However, the long-lasting monochloramine disinfectant (NH2Cl) can pose a significant risk to the environment. As, it can be introduced into stormwater sewers and thus freshwater sources. This study was intended to investigate decay of NH2Cl in stormwater networks starting by building a stormwater model and validating its hydraulic and hydrologic computations, and then modelling water quality in the storm sewers. The presented work here is only the first stage of this study. The 30th Avenue basin in Edmonton was chosen as a case study, because it has various land-use types including commercial, industrial, residential and parks. The City of Edmonton has already built a MIKE-URBAN stormwater model for modelling floods. However, this model was built to the trunk level where only the main drainage features were presented. Also, this model was not calibrated and known to consistently compute pipe flows higher than the observed values; not to the benefit of studying water quality. So the first goal was to complete modelling and updating the real stormwater network. Then, available GIS Data was used to calculate different catchment properties such as slope, length and imperviousness. To calibrate and validate this model, data of two temporary pipe flow monitoring stations was used along with records of two other permanent stations available for eight consecutive summer seasons. The effect of various hydrological parameters on model results was investigated. It was found that model results were affected by the ratio of impervious areas. The catchment length was tested, however calculated, because it is approximate representation of the catchment shape. Surface roughness coefficients were calibrated using. Consequently, computed flows at the two temporary locations had correlation coefficients of values 0.846 and 0.815, where the lower value pertained to the larger attached catchment area. Other statistical measures, such as peak error of 0.65%, volume error of 5.6%, maximum positive and negative differences of 2.17 and -1.63 respectively, were all found in acceptable ranges.

Recommendation Systems for Geoscience Data Portals Built by Analyzing Usage Patterns

NASA Astrophysics Data System (ADS)

Crosby, C.; Nandigam, V.; Baru, C.

2009-04-01

Since its launch five years ago, the National Science Foundation-funded GEON Project (www.geongrid.org) has been providing access to a variety of geoscience data sets such as geologic maps and other geographic information system (GIS)-oriented data, paleontologic databases, gravity and magnetics data and LiDAR topography via its online portal interface. In addition to data, the GEON Portal also provides web-based tools and other resources that enable users to process and interact with data. Examples of these tools include functions to dynamically map and integrate GIS data, compute synthetic seismograms, and to produce custom digital elevation models (DEMs) with user defined parameters such as resolution. The GEON portal built on the Gridsphere-portal framework allows us to capture user interaction with the system. In addition to the site access statistics captured by tools like Google Analystics which capture hits per unit time, search key words, operating systems, browsers, and referring sites, we also record additional statistics such as which data sets are being downloaded and in what formats, processing parameters, and navigation pathways through the portal. With over four years of data now available from the GEON Portal, this record of usage is a rich resource for exploring how earth scientists discover and utilize online data sets. Furthermore, we propose that this data could ultimately be harnessed to optimize the way users interact with the data portal, design intelligent processing and data management systems, and to make recommendations on algorithm settings and other available relevant data. The paradigm of integrating popular and commonly used patterns to make recommendations to a user is well established in the world of e-commerce where users receive suggestions on books, music and other products that they may find interesting based on their website browsing and purchasing history, as well as the patterns of fellow users who have made similar selections. However, this paradigm has not yet been explored for geoscience data portals. In this presentation we will present an initial analysis of user interaction and access statistics for the GEON OpenTopography LiDAR data distribution and processing system to illustrate what they reveal about user's spatial and temporal data access patterns, data processing parameter selections, and pathways through the data portal. We also demonstrate what these usage statistics can illustrate about aspects of the data sets that are of greatest interest. Finally, we explore how these usage statistics could be used to improve the user's experience in the data portal and to optimize how data access interfaces and tools are designed and implemented.

Supernova signatures of neutrino mass ordering

NASA Astrophysics Data System (ADS)

Scholberg, Kate

2018-01-01

A suite of detectors around the world is poised to measure the flavor-energy-time evolution of the ten-second burst of neutrinos from a core-collapse supernova occurring in the Milky Way or nearby. Next-generation detectors to be built in the next decade will have enhanced flavor sensitivity and statistics. Not only will the observation of this burst allow us to peer inside the dense matter of the extreme event and learn about the collapse processes and the birth of the remnant, but the neutrinos will bring information about neutrino properties themselves. This review surveys some of the physical signatures that the currently-unknown neutrino mass pattern will imprint on the observed neutrino events at Earth, emphasizing the most robust and least model-dependent signatures of mass ordering.

Code-division multiple-access multiuser demodulator by using quantum fluctuations.

PubMed

Otsubo, Yosuke; Inoue, Jun-Ichi; Nagata, Kenji; Okada, Masato

2014-07-01

We examine the average-case performance of a code-division multiple-access (CDMA) multiuser demodulator in which quantum fluctuations are utilized to demodulate the original message within the context of Bayesian inference. The quantum fluctuations are built into the system as a transverse field in the infinite-range Ising spin glass model. We evaluate the performance measurements by using statistical mechanics. We confirm that the CDMA multiuser modulator using quantum fluctuations achieve roughly the same performance as the conventional CDMA multiuser modulator through thermal fluctuations on average. We also find that the relationship between the quality of the original information retrieval and the amplitude of the transverse field is somehow a "universal feature" in typical probabilistic information processing, viz., in image restoration, error-correcting codes, and CDMA multiuser demodulation.

Code-division multiple-access multiuser demodulator by using quantum fluctuations

NASA Astrophysics Data System (ADS)

Otsubo, Yosuke; Inoue, Jun-ichi; Nagata, Kenji; Okada, Masato

2014-07-01

We examine the average-case performance of a code-division multiple-access (CDMA) multiuser demodulator in which quantum fluctuations are utilized to demodulate the original message within the context of Bayesian inference. The quantum fluctuations are built into the system as a transverse field in the infinite-range Ising spin glass model. We evaluate the performance measurements by using statistical mechanics. We confirm that the CDMA multiuser modulator using quantum fluctuations achieve roughly the same performance as the conventional CDMA multiuser modulator through thermal fluctuations on average. We also find that the relationship between the quality of the original information retrieval and the amplitude of the transverse field is somehow a "universal feature" in typical probabilistic information processing, viz., in image restoration, error-correcting codes, and CDMA multiuser demodulation.

Future Climate Change in the Baltic Sea Area

NASA Astrophysics Data System (ADS)

Bøssing Christensen, Ole; Kjellström, Erik; Zorita, Eduardo; Sonnenborg, Torben; Meier, Markus; Grinsted, Aslak

2015-04-01

Regional climate models have been used extensively since the first assessment of climate change in the Baltic Sea region published in 2008, not the least for studies of Europe (and including the Baltic Sea catchment area). Therefore, conclusions regarding climate model results have a better foundation than was the case for the first BACC report of 2008. This presentation will report model results regarding future climate. What is the state of understanding about future human-driven climate change? We will cover regional models, statistical downscaling, hydrological modelling, ocean modelling and sea-level change as it is projected for the Baltic Sea region. Collections of regional model simulations from the ENSEMBLES project for example, financed through the European 5th Framework Programme and the World Climate Research Programme Coordinated Regional Climate Downscaling Experiment, have made it possible to obtain an increasingly robust estimation of model uncertainty. While the first Baltic Sea assessment mainly used four simulations from the European 5th Framework Programme PRUDENCE project, an ensemble of 13 transient regional simulations with twice the horizontal resolution reaching the end of the 21st century has been available from the ENSEMBLES project; therefore it has been possible to obtain more quantitative assessments of model uncertainty. The literature about future climate change in the Baltic Sea region is largely built upon the ENSEMBLES project. Also within statistical downscaling, a considerable number of papers have been published, encompassing now the application of non-linear statistical models, projected changes in extremes and correction of climate model biases. The uncertainty of hydrological change has received increasing attention since the previous Baltic Sea assessment. Several studies on the propagation of uncertainties originating in GCMs, RCMs, and emission scenarios are presented. The number of studies on uncertainties related to downscaling and impact models is relatively small, but more are emerging. A large number of coupled climate-environmental scenario simulations for the Baltic Sea have been performed within the BONUS+ projects (ECOSUPPORT, INFLOW, AMBER and Baltic-C (2009-2011)), using various combinations of output from GCMs, RCMs, hydrological models and scenarios for load and emission of nutrients as forcing for Baltic Sea models. Such a large ensemble of scenario simulations for the Baltic Sea has never before been produced and enables for the first time an estimation of uncertainties.

A digital spatial predictive model of land-use change using economic and environmental inputs and a statistical tree classification approach: Thailand, 1970s--1990s

NASA Astrophysics Data System (ADS)

Felkner, John Sames

The scale and extent of global land use change is massive, and has potentially powerful effects on the global climate and global atmospheric composition (Turner & Meyer, 1994). Because of this tremendous change and impact, there is an urgent need for quantitative, empirical models of land use change, especially predictive models with an ability to capture the trajectories of change (Agarwal, Green, Grove, Evans, & Schweik, 2000; Lambin et al., 1999). For this research, a spatial statistical predictive model of land use change was created and run in two provinces of Thailand. The model utilized an extensive spatial database, and used a classification tree approach for explanatory model creation and future land use (Breiman, Friedman, Olshen, & Stone, 1984). Eight input variables were used, and the trees were run on a dependent variable of land use change measured from 1979 to 1989 using classified satellite imagery. The derived tree models were used to create probability of change surfaces, and these were then used to create predicted land cover maps for 1999. These predicted 1999 maps were compared with actual 1999 landcover derived from 1999 Landsat 7 imagery. The primary research hypothesis was that an explanatory model using both economic and environmental input variables would better predict future land use change than would either a model using only economic variables or a model using only environmental. Thus, the eight input variables included four economic and four environmental variables. The results indicated a very slight superiority of the full models to predict future agricultural change and future deforestation, but a slight superiority of the economic models to predict future built change. However, the margins of superiority were too small to be statistically significant. The resulting tree structures were used, however, to derive a series of principles or "rules" governing land use change in both provinces. The model was able to predict future land use, given a series of assumptions, with 90 percent overall accuracies. The model can be used in other developing or developed country locations for future land use prediction, determination of future threatened areas, or to derive "rules" or principles driving land use change.

Statistical modeling of urban air temperature distributions under different synoptic conditions

NASA Astrophysics Data System (ADS)

Beck, Christoph; Breitner, Susanne; Cyrys, Josef; Hald, Cornelius; Hartz, Uwe; Jacobeit, Jucundus; Richter, Katja; Schneider, Alexandra; Wolf, Kathrin

2015-04-01

Within urban areas air temperature may vary distinctly between different locations. These intra-urban air temperature variations partly reach magnitudes that are relevant with respect to human thermal comfort. Therefore and furthermore taking into account potential interrelations with other health related environmental factors (e.g. air quality) it is important to estimate spatial patterns of intra-urban air temperature distributions that may be incorporated into urban planning processes. In this contribution we present an approach to estimate spatial temperature distributions in the urban area of Augsburg (Germany) by means of statistical modeling. At 36 locations in the urban area of Augsburg air temperatures are measured with high temporal resolution (4 min.) since December 2012. These 36 locations represent different typical urban land use characteristics in terms of varying percentage coverages of different land cover categories (e.g. impervious, built-up, vegetated). Percentage coverages of these land cover categories have been extracted from different sources (Open Street Map, European Urban Atlas, Urban Morphological Zones) for regular grids of varying size (50, 100, 200 meter horizonal resolution) for the urban area of Augsburg. It is well known from numerous studies that land use characteristics have a distinct influence on air temperature and as well other climatic variables at a certain location. Therefore air temperatures at the 36 locations are modeled utilizing land use characteristics (percentage coverages of land cover categories) as predictor variables in Stepwise Multiple Regression models and in Random Forest based model approaches. After model evaluation via cross-validation appropriate statistical models are applied to gridded land use data to derive spatial urban air temperature distributions. Varying models are tested and applied for different seasons and times of the day and also for different synoptic conditions (e.g. clear and calm situations, cloudy and windy situations). Based on hourly air temperature data from our measurements in the urban area of Augsburg distinct temperature differences between locations with different urban land use characteristics are revealed. Under clear and calm weather conditions differences between mean hourly air temperatures reach values around 8°C. Whereas during cloudy and windy weather maximum differences in mean hourly air temperatures do not exceed 5°C. Differences appear usually slightly more pronounced in summer than in winter. First results from the application of statistical modeling approaches reveal promising skill of the models in terms of explained variances reaching up to 60% in leave-one-out cross-validation experiments. The contribution depicts the methodology of our approach and presents and discusses first results.

Spatial Supermarket Redlining and Neighborhood Vulnerability: A Case Study of Hartford, Connecticut

PubMed Central

Zhang, Mengyao

2015-01-01

The disinclination of chain supermarkets to locate or pull out existing stores from impoverished neighborhoods is termed as “supermarket redlining”. This paper attempts to map and understand the spatial effects of potential supermarket redlining on food vulnerability in urban disadvantaged neighborhoods of Hartford, Connecticut. Using a combination of statistical and spatial analysis functions, we first, built a Supermarket Redlining Index (SuRI) from five indicators such as sales volume, employee count, accepts food coupons from federally assisted programs, and size and population density of the service area to rank supermarkets in the order of their importance. Second, to understand the effect of redlining, a Supermarket Redlining Impact Model (SuRIM) was built with eleven indicators describing both the socioeconomic and food access vulnerabilities. The interaction of these vulnerabilities would identify the final outcome: neighborhoods where the impact of supermarket redlining would be critical. Results mapped critical areas in the inner-city of Hartford where if a nearby supermarket closes or relocates to a suburb with limited mitigation efforts to gill the grocery gap, a large number of minority, poor, and disadvantaged residents will experience difficulties to access healthy food leading to food insecurity or perhaps a food desert. We also suggest mitigation efforts to reduce the impact of large supermarket closures. PMID:27034615

Spatial Supermarket Redlining and Neighborhood Vulnerability: A Case Study of Hartford, Connecticut.

PubMed

Zhang, Mengyao; Debarchana, Ghosh

2016-02-01

The disinclination of chain supermarkets to locate or pull out existing stores from impoverished neighborhoods is termed as "supermarket redlining". This paper attempts to map and understand the spatial effects of potential supermarket redlining on food vulnerability in urban disadvantaged neighborhoods of Hartford, Connecticut. Using a combination of statistical and spatial analysis functions, we first, built a Supermarket Redlining Index (SuRI) from five indicators such as sales volume, employee count, accepts food coupons from federally assisted programs, and size and population density of the service area to rank supermarkets in the order of their importance. Second, to understand the effect of redlining, a Supermarket Redlining Impact Model (SuRIM) was built with eleven indicators describing both the socioeconomic and food access vulnerabilities. The interaction of these vulnerabilities would identify the final outcome: neighborhoods where the impact of supermarket redlining would be critical. Results mapped critical areas in the inner-city of Hartford where if a nearby supermarket closes or relocates to a suburb with limited mitigation efforts to gill the grocery gap, a large number of minority, poor, and disadvantaged residents will experience difficulties to access healthy food leading to food insecurity or perhaps a food desert. We also suggest mitigation efforts to reduce the impact of large supermarket closures.

Effects of land-use patterns on in-stream nitrogen in a highly-polluted river basin in Northeast China.

PubMed

Bu, Hongmei; Zhang, Yuan; Meng, Wei; Song, Xianfang

2016-05-15

This study investigated the effects of land-use patterns on nitrogen pollution in the Haicheng River basin in Northeast China during 2010 by conducting statistical and spatial analyses and by analyzing the isotopic composition of nitrate. Correlation and stepwise regressions indicated that land-use types and landscape metrics were correlated well with most river nitrogen variables and significantly predicted them during different sampling seasons. Built-up land use and shape metrics dominated in predicting nitrogen variables over seasons. According to the isotopic compositions of river nitrate in different zones, the nitrogen sources of the river principally originated from synthetic fertilizer, domestic sewage/manure, soil organic matter, and atmospheric deposition. Isotope mixing models indicated that source contributions of river nitrogen significantly varied from forested headwaters to densely populated towns of the river basin. Domestic sewage/manure was a major contributor to river nitrogen with the proportions of 76.4 ± 6.0% and 62.8 ± 2.1% in residence and farmland-residence zones, respectively. This research suggested that regulating built-up land uses and reducing discharges of domestic sewage and industrial wastewater would be effective methods for river nitrogen control. Copyright © 2016 Elsevier B.V. All rights reserved.

The Impact of Neighborhood Social and Built Environment Factors across the Cancer Continuum: Current Research, Methodologic Considerations, and Future Directions

PubMed Central

Gomez, Scarlett Lin; Shariff-Marco, Salma; De Rouen, Mindy; Keegan, Theresa H. M.; Yen, Irene H.; Mujahid, Mahasin; Satariano, William A.; Glaser, Sally L.

2015-01-01

Neighborhood social and built environments have been recognized as important contexts in which health is shaped. We review the extent to which these neighborhood factors have been addressed in population-level cancer research, with a scan of the literature for research that focuses on specific social and/or built environment characteristics and association with outcomes across the cancer continuum, including incidence, diagnosis, treatment, survivorship, and survival. We discuss commonalities and differences in methodologies across studies, current challenges in research methodology, and future directions in this research area. The assessment of social and built environment factors in relation to cancer is a relatively new field, with 82% of 34 reviewed papers published since 2010. Across the wide range of social and built environment exposures and cancer outcomes considered by the studies, numerous associations were reported. However, the directions and magnitudes of association varied, due in large part to the variation in cancer sites and outcomes being studied, but also likely due to differences in study populations, geographical region, and, importantly, choice of neighborhood measure and geographic scale. We recommend that future studies consider the life course implications of cancer incidence and survival, integrate secondary and self-report data, consider work neighborhood environments, and further develop analytical and statistical approaches appropriate to the geospatial and multilevel nature of the data. Incorporating social and built environment factors into research on cancer etiology and outcomes can provide insights into disease processes, identify vulnerable populations, and generate results with translational impact of relevance for interventionists and policy makers. PMID:25847484

The built environment moderates effects of family-based childhood obesity treatment over 2 years.

PubMed

Epstein, Leonard H; Raja, Samina; Daniel, Tinuke Oluyomi; Paluch, Rocco A; Wilfley, Denise E; Saelens, Brian E; Roemmich, James N

2012-10-01

Research suggests the neighborhood built environment is related to child physical activity and eating. The purpose of this study was to determine if characteristics of the neighborhood environment moderate the relationship between obesity treatment and weight loss, and if outcomes of particular treatments are moderated by built environment characteristics. The relationship between the built environment and standardized BMI (zBMI) changes for 191 8-12-year-old children who participated in one of four randomized, controlled trials of pediatric weight management was assessed using mixed models analysis of covariance. At 2-year follow-up, greater parkland, fewer convenience stores, and fewer supermarkets were associated with greater zBMI reduction across all interventions. No treatments interacted with characteristics of the built environment. Activity- and eating-related built neighborhood characteristics are associated with child success in behavioral obesity treatments. Efficacy may be improved by individualizing treatments based on built environment characteristics.

Classifying Vessels Operating in the South China Sea by Origin with the Automatic Identification System

DTIC Science & Technology

2018-03-01

operating vessel’s origin, by country and geographical region. Two types of models are built. The first model captures the naturally dependent nature of AIS... dependency between AIS signals in order to characterize maritime patterns of behavior by country and region. With relative accuracy, both types of...of models are built. The first model captures the naturally dependent nature of AIS signals and serves as a proof of concept for how well a global

Automatic segmentation of the facial nerve and chorda tympani in pediatric CT scans.

PubMed

Reda, Fitsum A; Noble, Jack H; Rivas, Alejandro; McRackan, Theodore R; Labadie, Robert F; Dawant, Benoit M

2011-10-01

Cochlear implant surgery is used to implant an electrode array in the cochlea to treat hearing loss. The authors recently introduced a minimally invasive image-guided technique termed percutaneous cochlear implantation. This approach achieves access to the cochlea by drilling a single linear channel from the outer skull into the cochlea via the facial recess, a region bounded by the facial nerve and chorda tympani. To exploit existing methods for computing automatically safe drilling trajectories, the facial nerve and chorda tympani need to be segmented. The goal of this work is to automatically segment the facial nerve and chorda tympani in pediatric CT scans. The authors have proposed an automatic technique to achieve the segmentation task in adult patients that relies on statistical models of the structures. These models contain intensity and shape information along the central axes of both structures. In this work, the authors attempted to use the same method to segment the structures in pediatric scans. However, the authors learned that substantial differences exist between the anatomy of children and that of adults, which led to poor segmentation results when an adult model is used to segment a pediatric volume. Therefore, the authors built a new model for pediatric cases and used it to segment pediatric scans. Once this new model was built, the authors employed the same segmentation method used for adults with algorithm parameters that were optimized for pediatric anatomy. A validation experiment was conducted on 10 CT scans in which manually segmented structures were compared to automatically segmented structures. The mean, standard deviation, median, and maximum segmentation errors were 0.23, 0.17, 0.18, and 1.27 mm, respectively. The results indicate that accurate segmentation of the facial nerve and chorda tympani in pediatric scans is achievable, thus suggesting that safe drilling trajectories can also be computed automatically.

Incorporating geologic information into hydraulic tomography: A general framework based on geostatistical approach

NASA Astrophysics Data System (ADS)

Zha, Yuanyuan; Yeh, Tian-Chyi J.; Illman, Walter A.; Onoe, Hironori; Mok, Chin Man W.; Wen, Jet-Chau; Huang, Shao-Yang; Wang, Wenke

2017-04-01

Hydraulic tomography (HT) has become a mature aquifer test technology over the last two decades. It collects nonredundant information of aquifer heterogeneity by sequentially stressing the aquifer at different wells and collecting aquifer responses at other wells during each stress. The collected information is then interpreted by inverse models. Among these models, the geostatistical approaches, built upon the Bayesian framework, first conceptualize hydraulic properties to be estimated as random fields, which are characterized by means and covariance functions. They then use the spatial statistics as prior information with the aquifer response data to estimate the spatial distribution of the hydraulic properties at a site. Since the spatial statistics describe the generic spatial structures of the geologic media at the site rather than site-specific ones (e.g., known spatial distributions of facies, faults, or paleochannels), the estimates are often not optimal. To improve the estimates, we introduce a general statistical framework, which allows the inclusion of site-specific spatial patterns of geologic features. Subsequently, we test this approach with synthetic numerical experiments. Results show that this approach, using conditional mean and covariance that reflect site-specific large-scale geologic features, indeed improves the HT estimates. Afterward, this approach is applied to HT surveys at a kilometer-scale-fractured granite field site with a distinct fault zone. We find that by including fault information from outcrops and boreholes for HT analysis, the estimated hydraulic properties are improved. The improved estimates subsequently lead to better prediction of flow during a different pumping test at the site.

Interactive and independent associations between the socioeconomic and objective built environment on the neighbourhood level and individual health: a systematic review of multilevel studies.

PubMed

Schüle, Steffen Andreas; Bolte, Gabriele

2015-01-01

The research question how contextual factors of neighbourhood environments influence individual health has gained increasing attention in public health research. Both socioeconomic neighbourhood characteristics and factors of the built environment play an important role for health and health-related behaviours. However, their reciprocal relationships have not been systematically reviewed so far. This systematic review aims to identify studies applying a multilevel modelling approach which consider both neighbourhood socioeconomic position (SEP) and factors of the objective built environment simultaneously in order to disentangle their independent and interactive effects on individual health. The three databases PubMed, PsycINFO, and Web of Science were systematically searched with terms for title and abstract screening. Grey literature was not included. Observational studies from USA, Canada, Australia, New Zealand, and Western European countries were considered which analysed simultaneously factors of neighbourhood SEP and the objective built environment with a multilevel modelling approach. Adjustment for individual SEP was a further inclusion criterion. Thirty-three studies were included in qualitative synthesis. Twenty-two studies showed an independent association between characteristics of neighbourhood SEP or the built environment and individual health outcomes or health-related behaviours. Twenty-one studies found cross-level or within-level interactions either between neighbourhood SEP and the built environment, or between neighbourhood SEP or the built environment and individual characteristics, such as sex, individual SEP or ethnicity. Due to the large variation of study design and heterogeneous reporting of results the identification of consistent findings was problematic and made quantitative analysis not possible. There is a need for studies considering multiple neighbourhood dimensions and applying multilevel modelling in order to clarify their causal relationship towards individual health. Especially, more studies using comparable characteristics of neighbourhood SEP and the objective built environment and analysing interactive effects are necessary to disentangle health impacts and identify vulnerable neighbourhoods and population groups.

Interactive and Independent Associations between the Socioeconomic and Objective Built Environment on the Neighbourhood Level and Individual Health: A Systematic Review of Multilevel Studies

PubMed Central

Schüle, Steffen Andreas; Bolte, Gabriele

2015-01-01

Background The research question how contextual factors of neighbourhood environments influence individual health has gained increasing attention in public health research. Both socioeconomic neighbourhood characteristics and factors of the built environment play an important role for health and health-related behaviours. However, their reciprocal relationships have not been systematically reviewed so far. This systematic review aims to identify studies applying a multilevel modelling approach which consider both neighbourhood socioeconomic position (SEP) and factors of the objective built environment simultaneously in order to disentangle their independent and interactive effects on individual health. Methods The three databases PubMed, PsycINFO, and Web of Science were systematically searched with terms for title and abstract screening. Grey literature was not included. Observational studies from USA, Canada, Australia, New Zealand, and Western European countries were considered which analysed simultaneously factors of neighbourhood SEP and the objective built environment with a multilevel modelling approach. Adjustment for individual SEP was a further inclusion criterion. Results Thirty-three studies were included in qualitative synthesis. Twenty-two studies showed an independent association between characteristics of neighbourhood SEP or the built environment and individual health outcomes or health-related behaviours. Twenty-one studies found cross-level or within-level interactions either between neighbourhood SEP and the built environment, or between neighbourhood SEP or the built environment and individual characteristics, such as sex, individual SEP or ethnicity. Due to the large variation of study design and heterogeneous reporting of results the identification of consistent findings was problematic and made quantitative analysis not possible. Conclusions There is a need for studies considering multiple neighbourhood dimensions and applying multilevel modelling in order to clarify their causal relationship towards individual health. Especially, more studies using comparable characteristics of neighbourhood SEP and the objective built environment and analysing interactive effects are necessary to disentangle health impacts and identify vulnerable neighbourhoods and population groups. PMID:25849569

Integrating Dynamic Data and Sensors with Semantic 3D City Models in the Context of Smart Cities

NASA Astrophysics Data System (ADS)

Chaturvedi, K.; Kolbe, T. H.

2016-10-01

Smart cities provide effective integration of human, physical and digital systems operating in the built environment. The advancements in city and landscape models, sensor web technologies, and simulation methods play a significant role in city analyses and improving quality of life of citizens and governance of cities. Semantic 3D city models can provide substantial benefits and can become a central information backbone for smart city infrastructures. However, current generation semantic 3D city models are static in nature and do not support dynamic properties and sensor observations. In this paper, we propose a new concept called Dynamizer allowing to represent highly dynamic data and providing a method for injecting dynamic variations of city object properties into the static representation. The approach also provides direct capability to model complex patterns based on statistics and general rules and also, real-time sensor observations. The concept is implemented as an Application Domain Extension for the CityGML standard. However, it could also be applied to other GML-based application schemas including the European INSPIRE data themes and national standards for topography and cadasters like the British Ordnance Survey Mastermap or the German cadaster standard ALKIS.

Discriminant analysis of fused positive and negative ion mobility spectra using multivariate self-modeling mixture analysis and neural networks.

PubMed

Chen, Ping; Harrington, Peter B

2008-02-01

A new method coupling multivariate self-modeling mixture analysis and pattern recognition has been developed to identify toxic industrial chemicals using fused positive and negative ion mobility spectra (dual scan spectra). A Smiths lightweight chemical detector (LCD), which can measure positive and negative ion mobility spectra simultaneously, was used to acquire the data. Simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) was used to separate the analytical peaks in the ion mobility spectra from the background reactant ion peaks (RIP). The SIMPLSIMA analytical components of the positive and negative ion peaks were combined together in a butterfly representation (i.e., negative spectra are reported with negative drift times and reflected with respect to the ordinate and juxtaposed with the positive ion mobility spectra). Temperature constrained cascade-correlation neural network (TCCCN) models were built to classify the toxic industrial chemicals. Seven common toxic industrial chemicals were used in this project to evaluate the performance of the algorithm. Ten bootstrapped Latin partitions demonstrated that the classification of neural networks using the SIMPLISMA components was statistically better than neural network models trained with fused ion mobility spectra (IMS).

Study on the medical meteorological forecast of the number of hypertension inpatient based on SVR

NASA Astrophysics Data System (ADS)

Zhai, Guangyu; Chai, Guorong; Zhang, Haifeng

2017-06-01

The purpose of this study is to build a hypertension prediction model by discussing the meteorological factors for hypertension incidence. The research method is selecting the standard data of relative humidity, air temperature, visibility, wind speed and air pressure of Lanzhou from 2010 to 2012(calculating the maximum, minimum and average value with 5 days as a unit ) as the input variables of Support Vector Regression(SVR) and the standard data of hypertension incidence of the same period as the output dependent variables to obtain the optimal prediction parameters by cross validation algorithm, then by SVR algorithm learning and training, a SVR forecast model for hypertension incidence is built. The result shows that the hypertension prediction model is composed of 15 input independent variables, the training accuracy is 0.005, the final error is 0.0026389. The forecast accuracy based on SVR model is 97.1429%, which is higher than statistical forecast equation and neural network prediction method. It is concluded that SVR model provides a new method for hypertension prediction with its simple calculation, small error as well as higher historical sample fitting and Independent sample forecast capability.

Toward better public health reporting using existing off the shelf approaches: The value of medical dictionaries in automated cancer detection using plaintext medical data.

PubMed

Kasthurirathne, Suranga N; Dixon, Brian E; Gichoya, Judy; Xu, Huiping; Xia, Yuni; Mamlin, Burke; Grannis, Shaun J

2017-05-01

Existing approaches to derive decision models from plaintext clinical data frequently depend on medical dictionaries as the sources of potential features. Prior research suggests that decision models developed using non-dictionary based feature sourcing approaches and "off the shelf" tools could predict cancer with performance metrics between 80% and 90%. We sought to compare non-dictionary based models to models built using features derived from medical dictionaries. We evaluated the detection of cancer cases from free text pathology reports using decision models built with combinations of dictionary or non-dictionary based feature sourcing approaches, 4 feature subset sizes, and 5 classification algorithms. Each decision model was evaluated using the following performance metrics: sensitivity, specificity, accuracy, positive predictive value, and area under the receiver operating characteristics (ROC) curve. Decision models parameterized using dictionary and non-dictionary feature sourcing approaches produced performance metrics between 70 and 90%. The source of features and feature subset size had no impact on the performance of a decision model. Our study suggests there is little value in leveraging medical dictionaries for extracting features for decision model building. Decision models built using features extracted from the plaintext reports themselves achieve comparable results to those built using medical dictionaries. Overall, this suggests that existing "off the shelf" approaches can be leveraged to perform accurate cancer detection using less complex Named Entity Recognition (NER) based feature extraction, automated feature selection and modeling approaches. Copyright © 2017 Elsevier Inc. All rights reserved.

Built Environment Influences of Children's Physical Activity: Examining Differences by Neighbourhood Size and Sex.

PubMed

Mitchell, Christine A; Clark, Andrew F; Gilliland, Jason A

2016-01-15

Neighbourhoods can facilitate or constrain moderate-to-vigorous physical activity (MVPA) among children by providing or restricting opportunities for MVPA. However, there is no consensus on how to define a child's neighbourhood. This study examines the influence of the neighbourhood built environment on objectively measured MVPA among 435 children (aged 9-14 years) in London (ON, Canada). As there is no consensus on how to delineate a child's neighbourhood, a geographic information system was used to generate measures of the neighbourhood built environment at two buffer sizes (500 m and 800 m) around each child's home. Linear regression models with robust standard errors (cluster) were used to analyze the relationship between built environment characteristics and average daily MVPA during non-school hours on weekdays. Sex-stratified models assessed sex-specific relationships. When accounting for individual and neighbourhood socio-demographic variables, park space and multi-use path space were found to influence children's MVPA. Sex-stratified models found significant associations between MVPA and park space, with the 800 m buffer best explaining boys' MVPA and the 500 m buffer best explaining girls' MVPA. Findings emphasize that, when designing built environments, programs, and policies to facilitate physical activity, it is important to consider that the size of the neighbourhood influencing a child's physical activity may differ according to sex.

The influence of the interactions between anthropogenic activities and multiple ecological factors on land surface temperatures of urban forests

NASA Astrophysics Data System (ADS)

Ren, Y.

2017-12-01

Context Land surface temperatures (LSTs) spatio-temporal distribution pattern of urban forests are influenced by many ecological factors; the identification of interaction between these factors can improve simulations and predictions of spatial patterns of urban cold islands. This quantitative research requires an integrated method that combines multiple sources data with spatial statistical analysis. Objectives The purpose of this study was to clarify urban forest LST influence interaction between anthropogenic activities and multiple ecological factors using cluster analysis of hot and cold spots and Geogdetector model. We introduced the hypothesis that anthropogenic activity interacts with certain ecological factors, and their combination influences urban forests LST. We also assumed that spatio-temporal distributions of urban forest LST should be similar to those of ecological factors and can be represented quantitatively. Methods We used Jinjiang as a representative city in China as a case study. Population density was employed to represent anthropogenic activity. We built up a multi-source data (forest inventory, digital elevation models (DEM), population, and remote sensing imagery) on a unified urban scale to support urban forest LST influence interaction research. Through a combination of spatial statistical analysis results, multi-source spatial data, and Geogdetector model, the interaction mechanisms of urban forest LST were revealed. Results Although different ecological factors have different influences on forest LST, in two periods with different hot spots and cold spots, the patch area and dominant tree species were the main factors contributing to LST clustering in urban forests. The interaction between anthropogenic activity and multiple ecological factors increased LST in urban forest stands, linearly and nonlinearly. Strong interactions between elevation and dominant species were generally observed and were prevalent in either hot or cold spots areas in different years. Conclusions In conclusion, a combination of spatial statistics and GeogDetector models should be effective for quantitatively evaluating interactive relationships among ecological factors, anthropogenic activity and LST.

Inferring gene regression networks with model trees

PubMed Central

2010-01-01

Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database) is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear regressions to separate areas of the search space favoring to infer localized similarities over a more global similarity. Furthermore, experimental results show the good performance of REGNET. PMID:20950452

[Fabrication and accuracy research on 3D printing dental model based on cone beam computed tomography digital modeling].

PubMed

Zhang, Hui-Rong; Yin, Le-Feng; Liu, Yan-Li; Yan, Li-Yi; Wang, Ning; Liu, Gang; An, Xiao-Li; Liu, Bin

2018-04-01

The aim of this study is to build a digital dental model with cone beam computed tomography (CBCT), to fabricate a virtual model via 3D printing, and to determine the accuracy of 3D printing dental model by comparing the result with a traditional dental cast. CBCT of orthodontic patients was obtained to build a digital dental model by using Mimics 10.01 and Geomagic studio software. The 3D virtual models were fabricated via fused deposition modeling technique (FDM). The 3D virtual models were compared with the traditional cast models by using a Vernier caliper. The measurements used for comparison included the width of each tooth, the length and width of the maxillary and mandibular arches, and the length of the posterior dental crest. 3D printing models had higher accuracy compared with the traditional cast models. The results of the paired t-test of all data showed that no statistically significant difference was observed between the two groups (P>0.05). Dental digital models built with CBCT realize the digital storage of patients' dental condition. The virtual dental model fabricated via 3D printing avoids traditional impression and simplifies the clinical examination process. The 3D printing dental models produced via FDM show a high degree of accuracy. Thus, these models are appropriate for clinical practice.

Bisous model-Detecting filamentary patterns in point processes

NASA Astrophysics Data System (ADS)

Tempel, E.; Stoica, R. S.; Kipper, R.; Saar, E.

2016-07-01

The cosmic web is a highly complex geometrical pattern, with galaxy clusters at the intersection of filaments and filaments at the intersection of walls. Identifying and describing the filamentary network is not a trivial task due to the overwhelming complexity of the structure, its connectivity and the intrinsic hierarchical nature. To detect and quantify galactic filaments we use the Bisous model, which is a marked point process built to model multi-dimensional patterns. The Bisous filament finder works directly with the galaxy distribution data and the model intrinsically takes into account the connectivity of the filamentary network. The Bisous model generates the visit map (the probability to find a filament at a given point) together with the filament orientation field. Using these two fields, we can extract filament spines from the data. Together with this paper we publish the computer code for the Bisous model that is made available in GitHub. The Bisous filament finder has been successfully used in several cosmological applications and further development of the model will allow to detect the filamentary network also in photometric redshift surveys, using the full redshift posterior. We also want to encourage the astro-statistical community to use the model and to connect it with all other existing methods for filamentary pattern detection and characterisation.

Multiple QSAR models, pharmacophore pattern and molecular docking analysis for anticancer activity of α, β-unsaturated carbonyl-based compounds, oxime and oxime ether analogues

NASA Astrophysics Data System (ADS)

Masand, Vijay H.; El-Sayed, Nahed N. E.; Bambole, Mukesh U.; Quazi, Syed A.

2018-04-01

Multiple discrete quantitative structure-activity relationships (QSARs) models were constructed for the anticancer activity of α, β-unsaturated carbonyl-based compounds, oxime and oxime ether analogues with a variety of substituents like sbnd Br, sbnd OH, -OMe, etc. at different positions. A big pool of descriptors was considered for QSAR model building. Genetic algorithm (GA), available in QSARINS-Chem, was executed to choose optimum number and set of descriptors to create the multi-linear regression equations for a dataset of sixty-nine compounds. The newly developed five parametric models were subjected to exhaustive internal and external validation along with Y-scrambling using QSARINS-Chem, according to the OECD principles for QSAR model validation. The models were built using easily interpretable descriptors and accepted after confirming statistically robustness with high external predictive ability. The five parametric models were found to have R2 = 0.80 to 0.86, R2ex = 0.75 to 0.84, and CCCex = 0.85 to 0.90. The models indicate that frequency of nitrogen and oxygen atoms separated by five bonds from each other and internal electronic environment of the molecule have correlation with the anticancer activity.

Methodological development for selection of significant predictors explaining fatal road accidents.

PubMed

Dadashova, Bahar; Arenas-Ramírez, Blanca; Mira-McWilliams, José; Aparicio-Izquierdo, Francisco

2016-05-01

Identification of the most relevant factors for explaining road accident occurrence is an important issue in road safety research, particularly for future decision-making processes in transport policy. However model selection for this particular purpose is still an ongoing research. In this paper we propose a methodological development for model selection which addresses both explanatory variable and adequate model selection issues. A variable selection procedure, TIM (two-input model) method is carried out by combining neural network design and statistical approaches. The error structure of the fitted model is assumed to follow an autoregressive process. All models are estimated using Markov Chain Monte Carlo method where the model parameters are assigned non-informative prior distributions. The final model is built using the results of the variable selection. For the application of the proposed methodology the number of fatal accidents in Spain during 2000-2011 was used. This indicator has experienced the maximum reduction internationally during the indicated years thus making it an interesting time series from a road safety policy perspective. Hence the identification of the variables that have affected this reduction is of particular interest for future decision making. The results of the variable selection process show that the selected variables are main subjects of road safety policy measures. Published by Elsevier Ltd.

Estimation of Total Nitrogen and Phosphorus in New England Streams Using Spatially Referenced Regression Models

USGS Publications Warehouse

Moore, Richard Bridge; Johnston, Craig M.; Robinson, Keith W.; Deacon, Jeffrey R.

2004-01-01

The U.S. Geological Survey (USGS), in cooperation with the U.S. Environmental Protection Agency (USEPA) and the New England Interstate Water Pollution Control Commission (NEIWPCC), has developed a water-quality model, called SPARROW (Spatially Referenced Regressions on Watershed Attributes), to assist in regional total maximum daily load (TMDL) and nutrient-criteria activities in New England. SPARROW is a spatially detailed, statistical model that uses regression equations to relate total nitrogen and phosphorus (nutrient) stream loads to nutrient sources and watershed characteristics. The statistical relations in these equations are then used to predict nutrient loads in unmonitored streams. The New England SPARROW models are built using a hydrologic network of 42,000 stream reaches and associated watersheds. Watershed boundaries are defined for each stream reach in the network through the use of a digital elevation model and existing digitized watershed divides. Nutrient source data is from permitted wastewater discharge data from USEPA's Permit Compliance System (PCS), various land-use sources, and atmospheric deposition. Physical watershed characteristics include drainage area, land use, streamflow, time-of-travel, stream density, percent wetlands, slope of the land surface, and soil permeability. The New England SPARROW models for total nitrogen and total phosphorus have R-squared values of 0.95 and 0.94, with mean square errors of 0.16 and 0.23, respectively. Variables that were statistically significant in the total nitrogen model include permitted municipal-wastewater discharges, atmospheric deposition, agricultural area, and developed land area. Total nitrogen stream-loss rates were significant only in streams with average annual flows less than or equal to 2.83 cubic meters per second. In streams larger than this, there is nondetectable in-stream loss of annual total nitrogen in New England. Variables that were statistically significant in the total phosphorus model include discharges for municipal wastewater-treatment facilities and pulp and paper facilities, developed land area, agricultural area, and forested area. For total phosphorus, loss rates were significant for reservoirs with surface areas of 10 square kilometers or less, and in streams with flows less than or equal to 2.83 cubic meters per second. Applications of SPARROW for evaluating nutrient loading in New England waters include estimates of the spatial distributions of total nitrogen and phosphorus yields, sources of the nutrients, and the potential for delivery of those yields to receiving waters. This information can be used to (1) predict ranges in nutrient levels in surface waters, (2) identify the environmental variables that are statistically significant predictors of nutrient levels in streams, (3) evaluate monitoring efforts for better determination of nutrient loads, and (4) evaluate management options for reducing nutrient loads to achieve water-quality goals.

Development of a Sigma-2 Receptor affinity filter through a Monte Carlo based QSAR analysis.

PubMed

Rescifina, Antonio; Floresta, Giuseppe; Marrazzo, Agostino; Parenti, Carmela; Prezzavento, Orazio; Nastasi, Giovanni; Dichiara, Maria; Amata, Emanuele

2017-08-30

For the first time in sigma-2 (σ 2 ) receptor field, a quantitative structure-activity relationship (QSAR) model has been built using pK i values of the whole set of known selective σ 2 receptor ligands (548 compounds), taken from the Sigma-2 Receptor Selective Ligands Database (S2RSLDB) (http://www.researchdsf.unict.it/S2RSLDB/), through the Monte Carlo technique and employing the software CORAL. The model has been developed by using a large and structurally diverse set of compounds, allowing for a prediction of different populations of chemical compounds endpoint (σ 2 receptor pK i ). The statistical quality reached, suggested that model for pK i determination is robust and possesses a satisfactory predictive potential. The statistical quality is high for both visible and invisible sets. The screening of the FDA approved drugs, external to our dataset, suggested that sixteen compounds might be repositioned as σ 2 receptor ligands (predicted pK i ≥8). A literature check showed that six of these compounds have already been tested for affinity at σ 2 receptor and, of these, two (Flunarizine and Terbinafine) have shown an experimental σ 2 receptor pK i >7. This suggests that this QSAR model may be used as focusing screening filter in order to prospectively find or repurpose new drugs with high affinity for the σ 2 receptor, and overall allowing for an enhanced hit rate respect to a random screening. Copyright © 2017 Elsevier B.V. All rights reserved.

Impact of the calibration period on the conceptual rainfall-runoff model parameter estimates

NASA Astrophysics Data System (ADS)

Todorovic, Andrijana; Plavsic, Jasna

2015-04-01

A conceptual rainfall-runoff model is defined by its structure and parameters, which are commonly inferred through model calibration. Parameter estimates depend on objective function(s), optimisation method, and calibration period. Model calibration over different periods may result in dissimilar parameter estimates, while model efficiency decreases outside calibration period. Problem of model (parameter) transferability, which conditions reliability of hydrologic simulations, has been investigated for decades. In this paper, dependence of the parameter estimates and model performance on calibration period is analysed. The main question that is addressed is: are there any changes in optimised parameters and model efficiency that can be linked to the changes in hydrologic or meteorological variables (flow, precipitation and temperature)? Conceptual, semi-distributed HBV-light model is calibrated over five-year periods shifted by a year (sliding time windows). Length of the calibration periods is selected to enable identification of all parameters. One water year of model warm-up precedes every simulation, which starts with the beginning of a water year. The model is calibrated using the built-in GAP optimisation algorithm. The objective function used for calibration is composed of Nash-Sutcliffe coefficient for flows and logarithms of flows, and volumetric error, all of which participate in the composite objective function with approximately equal weights. Same prior parameter ranges are used in all simulations. The model is calibrated against flows observed at the Slovac stream gauge on the Kolubara River in Serbia (records from 1954 to 2013). There are no trends in precipitation nor in flows, however, there is a statistically significant increasing trend in temperatures at this catchment. Parameter variability across the calibration periods is quantified in terms of standard deviations of normalised parameters, enabling detection of the most variable parameters. Correlation coefficients among optimised model parameters and total precipitation P, mean temperature T and mean flow Q are calculated to give an insight into parameter dependence on the hydrometeorological drivers. The results reveal high sensitivity of almost all model parameters towards calibration period. The highest variability is displayed by the refreezing coefficient, water holding capacity, and temperature gradient. The only statistically significant (decreasing) trend is detected in the evapotranspiration reduction threshold. Statistically significant correlation is detected between the precipitation gradient and precipitation depth, and between the time-area histogram base and flows. All other correlations are not statistically significant, implying that changes in optimised parameters cannot generally be linked to the changes in P, T or Q. As for the model performance, the model reproduces the observed runoff satisfactorily, though the runoff is slightly overestimated in wet periods. The Nash-Sutcliffe efficiency coefficient (NSE) ranges from 0.44 to 0.79. Higher NSE values are obtained over wetter periods, what is supported by statistically significant correlation between NSE and flows. Overall, no systematic variations in parameters or in model performance are detected. Parameter variability may therefore rather be attributed to errors in data or inadequacies in the model structure. Further research is required to examine the impact of the calibration strategy or model structure on the variability in optimised parameters in time.

Gaussian process based modeling and experimental design for sensor calibration in drifting environments

PubMed Central

Geng, Zongyu; Yang, Feng; Chen, Xi; Wu, Nianqiang

2016-01-01

It remains a challenge to accurately calibrate a sensor subject to environmental drift. The calibration task for such a sensor is to quantify the relationship between the sensor’s response and its exposure condition, which is specified by not only the analyte concentration but also the environmental factors such as temperature and humidity. This work developed a Gaussian Process (GP)-based procedure for the efficient calibration of sensors in drifting environments. Adopted as the calibration model, GP is not only able to capture the possibly nonlinear relationship between the sensor responses and the various exposure-condition factors, but also able to provide valid statistical inference for uncertainty quantification of the target estimates (e.g., the estimated analyte concentration of an unknown environment). Built on GP’s inference ability, an experimental design method was developed to achieve efficient sampling of calibration data in a batch sequential manner. The resulting calibration procedure, which integrates the GP-based modeling and experimental design, was applied on a simulated chemiresistor sensor to demonstrate its effectiveness and its efficiency over the traditional method. PMID:26924894

Operational use of high-resolution sst in a coupled sea ice-ocean model

NASA Astrophysics Data System (ADS)

Albretsen, A.

2003-04-01

A high-latitude, near real time, sea surface temperature (SST) product with 10 km resolution is developed at the Norwegian Meteorological Institute (met.no) through the EUMETSAT project OSI-SAF (Ocean and Sea Ice Satellite Application Facility). The product covers the Atlantic Ocean from 50N to 90N and is produced twice daily. A digitized SST and sea ice map is produced manually once a week at the Ice Mapping Service at met.no using all available information from the previous week. This map is the basis for a daily SST analysis, in which the most recent OSI-SAF SST products are successively overlaid. The resulting SST analysis field is then used in a simple data assimilation scheme in a coupled ice-ocean model to perform daily 10 days forecasts of ocean and sea ice variables. Also, the associated OSI-SAF sea ice concentration product, built from different polar orbiting satellites, is assimilated into the sea ice model. Preliminary estimates of impact on forecast skill and error statistics will be presented.

Personality, emotion-related variables, and media pressure predict eating disorders via disordered eating in Lebanese university students.

PubMed

Sanchez-Ruiz, Maria Jose; El-Jor, Claire; Abi Kharma, Joelle; Bassil, Maya; Zeeni, Nadine

2017-04-18

Disordered eating behaviors are on the rise among youth. The present study investigates psychosocial and weight-related variables as predictors of eating disorders (ED) through disordered eating (DE) dimensions (namely restrained, external, and emotional eating) in Lebanese university students. The sample consisted of 244 undergraduates (143 female) aged from 18 to 31 years (M = 20.06; SD = 1.67). Using path analysis, two statistical models were built separately with restrained and emotional eating as dependent variables, and all possible direct and indirect pathways were tested for mediating effects. The variables tested for were media influence, perfectionism, trait emotional intelligence, and the Big Five dimensions. In the first model, media pressure, self-control, and extraversion predicted eating disorders via emotional eating. In the second model, media pressure and perfectionism predicted eating disorders via restrained eating. Findings from this study provide an understanding of the dynamics between DE, ED, and key personality, emotion-related, and social factors in youth. Lastly, implications and recommendations for future studies are advanced.

Effective Perron-Frobenius eigenvalue for a correlated random map

NASA Astrophysics Data System (ADS)

Pool, Roman R.; Cáceres, Manuel O.

2010-09-01

We investigate the evolution of random positive linear maps with various type of disorder by analytic perturbation and direct simulation. Our theoretical result indicates that the statistics of a random linear map can be successfully described for long time by the mean-value vector state. The growth rate can be characterized by an effective Perron-Frobenius eigenvalue that strongly depends on the type of correlation between the elements of the projection matrix. We apply this approach to an age-structured population dynamics model. We show that the asymptotic mean-value vector state characterizes the population growth rate when the age-structured model has random vital parameters. In this case our approach reveals the nontrivial dependence of the effective growth rate with cross correlations. The problem was reduced to the calculation of the smallest positive root of a secular polynomial, which can be obtained by perturbations in terms of Green’s function diagrammatic technique built with noncommutative cumulants for arbitrary n -point correlations.

PDB_REDO: constructive validation, more than just looking for errors.

PubMed

Joosten, Robbie P; Joosten, Krista; Murshudov, Garib N; Perrakis, Anastassis

2012-04-01

Developments of the PDB_REDO procedure that combine re-refinement and rebuilding within a unique decision-making framework to improve structures in the PDB are presented. PDB_REDO uses a variety of existing and custom-built software modules to choose an optimal refinement protocol (e.g. anisotropic, isotropic or overall B-factor refinement, TLS model) and to optimize the geometry versus data-refinement weights. Next, it proceeds to rebuild side chains and peptide planes before a final optimization round. PDB_REDO works fully automatically without the need for intervention by a crystallographic expert. The pipeline was tested on 12 000 PDB entries and the great majority of the test cases improved both in terms of crystallographic criteria such as R(free) and in terms of widely accepted geometric validation criteria. It is concluded that PDB_REDO is useful to update the otherwise `static' structures in the PDB to modern crystallographic standards. The publically available PDB_REDO database provides better model statistics and contributes to better refinement and validation targets.

Long-Term PMD Characterization of a Metropolitan G.652 Fiber Plant

NASA Astrophysics Data System (ADS)

Poggiolini, Pierluigi; Nespola, Antonino; Abrate, Silvio; Ferrero, Valter; Lezzi, C.

2006-11-01

Using the Jones matrix eigenanalysis method, the differential group delay (DGD) behavior of a metropolitan G.652 buried operational cable plant of a major Italian telecom operator in the city of Torino was characterized. The measurement campaign lasted 73 consecutive days. An extremely stable DGD behavior was found, despite the fact that the cables run across a very densely built city environment. On the other hand, it was also found that routine maintenance on the infrastructure, although indirect and nondisruptive, repeatedly altered this picture. Based on these results, it is argued that the recently introduced “hinge” model describes the plant DGD statistical behavior better than the traditional description. This argument is also supported by the close fit that was obtained with a theoretical hinge-model DGD probability density function of the measured DGD data histogram. It is also argued that in this kind of scenario fast adaptive compensation is most likely the only realistic solution for avoiding sudden performance degradation or out-of-service.

A Decision Tree for Nonmetric Sex Assessment from the Skull.

PubMed

Langley, Natalie R; Dudzik, Beatrix; Cloutier, Alesia

2018-01-01

This study uses five well-documented cranial nonmetric traits (glabella, mastoid process, mental eminence, supraorbital margin, and nuchal crest) and one additional trait (zygomatic extension) to develop a validated decision tree for sex assessment. The decision tree was built and cross-validated on a sample of 293 U.S. White individuals from the William M. Bass Donated Skeletal Collection. Ordinal scores from the six traits were analyzed using the partition modeling option in JMP Pro 12. A holdout sample of 50 skulls was used to test the model. The most accurate decision tree includes three variables: glabella, zygomatic extension, and mastoid process. This decision tree yielded 93.5% accuracy on the training sample, 94% on the cross-validated sample, and 96% on a holdout validation sample. Linear weighted kappa statistics indicate acceptable agreement among observers for these variables. Mental eminence should be avoided, and definitions and figures should be referenced carefully to score nonmetric traits. © 2017 American Academy of Forensic Sciences.

PDB_REDO: constructive validation, more than just looking for errors

PubMed Central

Joosten, Robbie P.; Joosten, Krista; Murshudov, Garib N.; Perrakis, Anastassis

2012-01-01

Developments of the PDB_REDO procedure that combine re-refinement and rebuilding within a unique decision-making framework to improve structures in the PDB are presented. PDB_REDO uses a variety of existing and custom-built software modules to choose an optimal refinement protocol (e.g. anisotropic, isotropic or overall B-factor refinement, TLS model) and to optimize the geometry versus data-refinement weights. Next, it proceeds to rebuild side chains and peptide planes before a final optimization round. PDB_REDO works fully automatically without the need for intervention by a crystallographic expert. The pipeline was tested on 12 000 PDB entries and the great majority of the test cases improved both in terms of crystallographic criteria such as R free and in terms of widely accepted geometric validation criteria. It is concluded that PDB_REDO is useful to update the otherwise ‘static’ structures in the PDB to modern crystallographic standards. The publically available PDB_REDO database provides better model statistics and contributes to better refinement and validation targets. PMID:22505269

Cognitive control related network analysis: A novel way to measure neuron fiber connection of Alzheimer's disease.

PubMed

Changle Zhang; Tao Chai; Na Gao; Ma, Heather T

2017-07-01

Effective measurement of cognitive impairment caused by Alzheimer's disease (AD) will provide a chance for early medical intervention and delay the disease onset. Diffusion tensor imaging (DTI) provides a non-intrusive examination of cranial nerve diseases which can help us observe the microstructure of neuron fibers. Cognitive control network (CCN) consists of the brain regions that highly related to human self-control. In this study, hub-and-spoke model which was widely used in transportation and sociology area had been employed to analyze the relationship of CCN and other regions under its control, cognitive control related network (CCRN) was built by applying this model. Local and global graph theoretical parameters were calculated and went through statistical analysis. Significant difference had been found in the scale of local as well as global which may represent the impairment of cognitive control ability. This result may provide a potential bio-marker for the loss of connection caused by Alzheimer's disease.

Predicting fiber refractive index from a measured preform index profile

NASA Astrophysics Data System (ADS)

Kiiveri, P.; Koponen, J.; Harra, J.; Novotny, S.; Husu, H.; Ihalainen, H.; Kokki, T.; Aallos, V.; Kimmelma, O.; Paul, J.

2018-02-01

When producing fiber lasers and amplifiers, silica glass compositions consisting of three to six different materials are needed. Due to the varying needs of different applications, substantial number of different glass compositions are used in the active fiber structures. Often it is not possible to find material parameters for theoretical models to estimate thermal and mechanical properties of those glass compositions. This makes it challenging to predict accurately fiber core refractive index values, even if the preform index profile is measured. Usually the desired fiber refractive index value is achieved experimentally, which is expensive. To overcome this problem, we analyzed statistically the changes between the measured preform and fiber index values. We searched for correlations that would help to predict the Δn-value change from preform to fiber in a situation where we don't know the values of the glass material parameters that define the change. Our index change models were built using the data collected from preforms and fibers made by the Direct Nanoparticle Deposition (DND) technology.

Using Green Building As A Model For Making Health Promotion Standard In The Built Environment.

PubMed

Trowbridge, Matthew J; Worden, Kelly; Pyke, Christopher

2016-11-01

The built environment-the constructed physical parts of the places where people live and work-is a powerful determinant of both individual and population health. Awareness of the link between place and health is growing within the public health sector and among built environment decision makers working in design, construction, policy, and both public and private finance. However, these decision makers lack the knowledge, tools, and capacity to ensure that health and well-being are routinely considered across all sectors of the built environment. The green building industry has successfully established environmental sustainability as a normative part of built environment practice, policy making, and investment. We explore the value of this industry's experience as a template for promoting health and well-being in the built environment. Project HOPE—The People-to-People Health Foundation, Inc.

Rapid model building of beta-sheets in electron-density maps.

PubMed

Terwilliger, Thomas C

2010-03-01

A method for rapidly building beta-sheets into electron-density maps is presented. beta-Strands are identified as tubes of high density adjacent to and nearly parallel to other tubes of density. The alignment and direction of each strand are identified from the pattern of high density corresponding to carbonyl and C(beta) atoms along the strand averaged over all repeats present in the strand. The beta-strands obtained are then assembled into a single atomic model of the beta-sheet regions. The method was tested on a set of 42 experimental electron-density maps at resolutions ranging from 1.5 to 3.8 A. The beta-sheet regions were nearly completely built in all but two cases, the exceptions being one structure at 2.5 A resolution in which a third of the residues in beta-sheets were built and a structure at 3.8 A in which under 10% were built. The overall average r.m.s.d. of main-chain atoms in the residues built using this method compared with refined models of the structures was 1.5 A.

Modeling urbanization patterns at a global scale with generative adversarial networks

NASA Astrophysics Data System (ADS)

Albert, A. T.; Strano, E.; Gonzalez, M.

2017-12-01

Current demographic projections show that, in the next 30 years, global population growth will mostly take place in developing countries. Coupled with a decrease in density, such population growth could potentially double the land occupied by settlements by 2050. The lack of reliable and globally consistent socio-demographic data, coupled with the limited predictive performance underlying traditional urban spatial explicit models, call for developing better predictive methods, calibrated using a globally-consistent dataset. Thus, richer models of the spatial interplay between the urban built-up land, population distribution and energy use are central to the discussion around the expansion and development of cities, and their impact on the environment in the context of a changing climate. In this talk we discuss methods for, and present an analysis of, urban form, defined as the spatial distribution of macroeconomic quantities that characterize a city, using modern machine learning methods and best-available remote-sensing data for the world's largest 25,000 cities. We first show that these cities may be described by a small set of patterns in radial building density, nighttime luminosity, and population density, which highlight, to first order, differences in development and land use across the world. We observe significant, spatially-dependent variance around these typical patterns, which would be difficult to model using traditional statistical methods. We take a first step in addressing this challenge by developing CityGAN, a conditional generative adversarial network model for simulating realistic urban forms. To guide learning and measure the quality of the simulated synthetic cities, we develop a specialized loss function for GAN optimization that incorporates standard spatial statistics used by urban analysis experts. Our framework is a stark departure from both the standard physics-based approaches in the literature (that view urban forms as fractals with a scale-free behavior), and the traditional statistical learning approaches (whereby values of individual pixels are modeled as functions of locally-defined, hand-engineered features). This is a first-of-its-kind analysis of urban forms using data at a planetary scale.

Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore

PubMed Central

Shi, Yuan; Liu, Xu; Kok, Suet-Yheng; Rajarethinam, Jayanthi; Liang, Shaohong; Yap, Grace; Chong, Chee-Seng; Lee, Kim-Sung; Tan, Sharon S.Y.; Chin, Christopher Kuan Yew; Lo, Andrew; Kong, Waiming; Ng, Lee Ching; Cook, Alex R.

2015-01-01

Background: With its tropical rainforest climate, rapid urbanization, and changing demography and ecology, Singapore experiences endemic dengue; the last large outbreak in 2013 culminated in 22,170 cases. In the absence of a vaccine on the market, vector control is the key approach for prevention. Objectives: We sought to forecast the evolution of dengue epidemics in Singapore to provide early warning of outbreaks and to facilitate the public health response to moderate an impending outbreak. Methods: We developed a set of statistical models using least absolute shrinkage and selection operator (LASSO) methods to forecast the weekly incidence of dengue notifications over a 3-month time horizon. This forecasting tool used a variety of data streams and was updated weekly, including recent case data, meteorological data, vector surveillance data, and population-based national statistics. The forecasting methodology was compared with alternative approaches that have been proposed to model dengue case data (seasonal autoregressive integrated moving average and step-down linear regression) by fielding them on the 2013 dengue epidemic, the largest on record in Singapore. Results: Operationally useful forecasts were obtained at a 3-month lag using the LASSO-derived models. Based on the mean average percentage error, the LASSO approach provided more accurate forecasts than the other methods we assessed. We demonstrate its utility in Singapore’s dengue control program by providing a forecast of the 2013 outbreak for advance preparation of outbreak response. Conclusions: Statistical models built using machine learning methods such as LASSO have the potential to markedly improve forecasting techniques for recurrent infectious disease outbreaks such as dengue. Citation: Shi Y, Liu X, Kok SY, Rajarethinam J, Liang S, Yap G, Chong CS, Lee KS, Tan SS, Chin CK, Lo A, Kong W, Ng LC, Cook AR. 2016. Three-month real-time dengue forecast models: an early warning system for outbreak alerts and policy decision support in Singapore. Environ Health Perspect 124:1369–1375; http://dx.doi.org/10.1289/ehp.1509981 PMID:26662617

Assessing image quality of low-cost laparoscopic box trainers: options for residents training at home.

PubMed

Kiely, Daniel J; Stephanson, Kirk; Ross, Sue

2011-10-01

Low-cost laparoscopic box trainers built using home computers and webcams may provide residents with a useful tool for practice at home. This study set out to evaluate the image quality of low-cost laparoscopic box trainers compared with a commercially available model. Five low-cost laparoscopic box trainers including the components listed were compared in random order to one commercially available box trainer: A (high-definition USB 2.0 webcam, PC laptop), B (Firewire webcam, Mac laptop), C (high-definition USB 2.0 webcam, Mac laptop), D (standard USB webcam, PC desktop), E (Firewire webcam, PC desktop), and F (the TRLCD03 3-DMEd Standard Minimally Invasive Training System). Participants observed still image quality and performed a peg transfer task using each box trainer. Participants rated still image quality, image quality with motion, and whether the box trainer had sufficient image quality to be useful for training. Sixteen residents in obstetrics and gynecology took part in the study. The box trainers showing no statistically significant difference from the commercially available model were A, B, C, D, and E for still image quality; A for image quality with motion; and A and B for usefulness of the simulator based on image quality. The cost of the box trainers A-E is approximately $100 to $160 each, not including a computer or laparoscopic instruments. Laparoscopic box trainers built from a high-definition USB 2.0 webcam with a PC (box trainer A) or from a Firewire webcam with a Mac (box trainer B) provide image quality comparable with a commercial standard.

Artificial Neural Network Approach in Laboratory Test Reporting:  Learning Algorithms.

PubMed

Demirci, Ferhat; Akan, Pinar; Kume, Tuncay; Sisman, Ali Riza; Erbayraktar, Zubeyde; Sevinc, Suleyman

2016-08-01

In the field of laboratory medicine, minimizing errors and establishing standardization is only possible by predefined processes. The aim of this study was to build an experimental decision algorithm model open to improvement that would efficiently and rapidly evaluate the results of biochemical tests with critical values by evaluating multiple factors concurrently. The experimental model was built by Weka software (Weka, Waikato, New Zealand) based on the artificial neural network method. Data were received from Dokuz Eylül University Central Laboratory. "Training sets" were developed for our experimental model to teach the evaluation criteria. After training the system, "test sets" developed for different conditions were used to statistically assess the validity of the model. After developing the decision algorithm with three iterations of training, no result was verified that was refused by the laboratory specialist. The sensitivity of the model was 91% and specificity was 100%. The estimated κ score was 0.950. This is the first study based on an artificial neural network to build an experimental assessment and decision algorithm model. By integrating our trained algorithm model into a laboratory information system, it may be possible to reduce employees' workload without compromising patient safety. © American Society for Clinical Pathology, 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Validity, Reliability and Difficulty Indices for Instructor-Built Exam Questions

ERIC Educational Resources Information Center

Jandaghi, Gholamreza; Shaterian, Fatemeh

2008-01-01

The purpose of the research is to determine college Instructor's skill rate in designing exam questions in chemistry subject. The statistical population was all of chemistry exam sheets for two semesters in one academic year from which a sample of 364 exam sheets was drawn using multistage cluster sampling. Two experts assessed the sheets and by…

Observing Ocean Ecosystems with Sonar

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matzner, Shari; Maxwell, Adam R.; Ham, Kenneth D.

2016-12-01

We present a real-time processing system for sonar to detect and track animals, and to extract water column biomass statistics in order to facilitate continuous monitoring of an underwater environment. The Nekton Interaction Monitoring System (NIMS) is built to connect to an instrumentation network, where it consumes a real-time stream of sonar data and archives tracking and biomass data.

Teaching Inferential Statistics to Social Work Students: A Decision-Making Flow Chart

ERIC Educational Resources Information Center

Calderwood, Kimberly A.

2012-01-01

Given that social work research courses are typically built on modernist principles of teaching and content, it is not surprising that the majority of social work students dread these courses. Few attempts have been made to better align the modernist content of quantitative research with the postmodern philosophy and values inherent in current…

Design of Soil Salinity Policies with Tinamit, a Flexible and Rapid Tool to Couple Stakeholder-Built System Dynamics Models with Physically-Based Models

NASA Astrophysics Data System (ADS)

Malard, J. J.; Baig, A. I.; Hassanzadeh, E.; Adamowski, J. F.; Tuy, H.; Melgar-Quiñonez, H.

2016-12-01

Model coupling is a crucial step to constructing many environmental models, as it allows for the integration of independently-built models representing different system sub-components to simulate the entire system. Model coupling has been of particular interest in combining socioeconomic System Dynamics (SD) models, whose visual interface facilitates their direct use by stakeholders, with more complex physically-based models of the environmental system. However, model coupling processes are often cumbersome and inflexible and require extensive programming knowledge, limiting their potential for continued use by stakeholders in policy design and analysis after the end of the project. Here, we present Tinamit, a flexible Python-based model-coupling software tool whose easy-to-use API and graphical user interface make the coupling of stakeholder-built SD models with physically-based models rapid, flexible and simple for users with limited to no coding knowledge. The flexibility of the system allows end users to modify the SD model as well as the linking variables between the two models themselves with no need for recoding. We use Tinamit to couple a stakeholder-built socioeconomic model of soil salinization in Pakistan with the physically-based soil salinity model SAHYSMOD. As climate extremes increase in the region, policies to slow or reverse soil salinity buildup are increasing in urgency and must take both socioeconomic and biophysical spheres into account. We use the Tinamit-coupled model to test the impact of integrated policy options (economic and regulatory incentives to farmers) on soil salinity in the region in the face of future climate change scenarios. Use of the Tinamit model allowed for rapid and flexible coupling of the two models, allowing the end user to continue making model structure and policy changes. In addition, the clear interface (in contrast to most model coupling code) makes the final coupled model easily accessible to stakeholders with limited technical background.

Distinguishing humans from computers in the game of go: A complex network approach

NASA Astrophysics Data System (ADS)

Coquidé, C.; Georgeot, B.; Giraud, O.

2017-08-01

We compare complex networks built from the game of go and obtained from databases of human-played games with those obtained from computer-played games. Our investigations show that statistical features of the human-based networks and the computer-based networks differ, and that these differences can be statistically significant on a relatively small number of games using specific estimators. We show that the deterministic or stochastic nature of the computer algorithm playing the game can also be distinguished from these quantities. This can be seen as a tool to implement a Turing-like test for go simulators.

The impact of neighborhood social and built environment factors across the cancer continuum: Current research, methodological considerations, and future directions.

PubMed

Gomez, Scarlett Lin; Shariff-Marco, Salma; DeRouen, Mindy; Keegan, Theresa H M; Yen, Irene H; Mujahid, Mahasin; Satariano, William A; Glaser, Sally L

2015-07-15

Neighborhood social and built environments have been recognized as important contexts in which health is shaped. The authors reviewed the extent to which these neighborhood factors have been addressed in population-level cancer research by scanning the literature for research focused on specific social and/or built environment characteristics and their association with outcomes across the cancer continuum, including incidence, diagnosis, treatment, survivorship, and survival. The commonalities and differences in methodologies across studies, the current challenges in research methodology, and future directions in this research also were addressed. The assessment of social and built environment factors in relation to cancer is a relatively new field, with 82% of the 34 reviewed articles published since 2010. Across the wide range of social and built environment exposures and cancer outcomes considered by the studies, numerous associations were reported. However, the directions and magnitudes of associations varied, in large part because of the variation in cancer sites and outcomes studied, but also likely because of differences in study populations, geographic regions, and, importantly, choice of neighborhood measures and geographic scales. The authors recommend that future studies consider the life-course implications of cancer incidence and survival, integrate secondary and self-report data, consider work neighborhood environments, and further develop analytical and statistical approaches appropriate to the geospatial and multilevel nature of the data. Incorporating social and built environment factors into research on cancer etiology and outcomes can provide insights into disease processes, identify vulnerable populations, and generate results with translational impact of relevance for interventionists and policy makers. © 2015 American Cancer Society.

Study of the Effect of Lubricant Emulsion Percentage and Tool Material on Surface Roughness in Machining of EN-AC 48000 Alloy

NASA Astrophysics Data System (ADS)

Soltani, E.; Shahali, H.; Zarepour, H.

2011-01-01

In this paper, the effect of machining parameters, namely, lubricant emulsion percentage and tool material on surface roughness has been studied in machining process of EN-AC 48000 aluminum alloy. EN-AC 48000 aluminum alloy is an important alloy in industries. Machining of this alloy is of vital importance due to built-up edge and tool wear. A L9 Taguchi standard orthogonal array has been applied as experimental design to investigate the effect of the factors and their interaction. Nine machining tests have been carried out with three random replications resulting in 27 experiments. Three type of cutting tools including coated carbide (CD1810), uncoated carbide (H10), and polycrystalline diamond (CD10) have been used in this research. Emulsion percentage of lubricant is selected at three levels including 3%, 5% and 10%. Statistical analysis has been employed to study the effect of factors and their interactions using ANOVA method. Moreover, the optimal factors level has been achieved through signal to noise ratio (S/N) analysis. Also, a regression model has been provided to predict the surface roughness. Finally, the results of the confirmation tests have been presented to verify the adequacy of the predictive model. In this research, surface quality was improved by 9% using lubricant and statistical optimization method.

Building and using a statistical 3D motion atlas for analyzing myocardial contraction in MRI

NASA Astrophysics Data System (ADS)

Rougon, Nicolas F.; Petitjean, Caroline; Preteux, Francoise J.

2004-05-01

We address the issue of modeling and quantifying myocardial contraction from 4D MR sequences, and present an unsupervised approach for building and using a statistical 3D motion atlas for the normal heart. This approach relies on a state-of-the-art variational non rigid registration (NRR) technique using generalized information measures, which allows for robust intra-subject motion estimation and inter-subject anatomical alignment. The atlas is built from a collection of jointly acquired tagged and cine MR exams in short- and long-axis views. Subject-specific non parametric motion estimates are first obtained by incremental NRR of tagged images onto the end-diastolic (ED) frame. Individual motion data are then transformed into the coordinate system of a reference subject using subject-to-reference mappings derived by NRR of cine ED images. Finally, principal component analysis of aligned motion data is performed for each cardiac phase, yielding a mean model and a set of eigenfields encoding kinematic ariability. The latter define an organ-dedicated hierarchical motion basis which enables parametric motion measurement from arbitrary tagged MR exams. To this end, the atlas is transformed into subject coordinates by reference-to-subject NRR of ED cine frames. Atlas-based motion estimation is then achieved by parametric NRR of tagged images onto the ED frame, yielding a compact description of myocardial contraction during diastole.

Pulse Code Modulation (PCM) data storage and analysis using a microcomputer

NASA Technical Reports Server (NTRS)

Massey, D. E.

1986-01-01

A PCM storage device/data analyzer is described. This instrument is a peripheral plug-in board especially built to enable a personal computer to store and analyze data from a PCM source. This board and custom written software turns a computer into a snapshot PCM decommutator. This instrument will take in and store many hundreds or thousands of PCM telemetry data frames, then sift through them over and over again. The data can be converted to any number base and displayed, examined for any bit dropouts or changes in particular words or frames, graphically plotted, or statistically analyzed. This device was designed and built for use on the NASA Sounding Rocket Program for PCM encoder configuration and testing.

Final excitation energy of fission fragments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schmidt, Karl-Heinz; Jurado, Beatriz

We study how the excitation energy of the fully accelerated fission fragments is built up. It is stressed that only the intrinsic excitation energy available before scission can be exchanged between the fission fragments to achieve thermal equilibrium. This is in contradiction with most models used to calculate prompt neutron emission, where it is assumed that the total excitation energy of the final fragments is shared between the fragments by the condition of equal temperatures. We also study the intrinsic excitation-energy partition in statistical equilibrium for different level-density descriptions as a function of the total intrinsic excitation energy of themore » fissioning system. Excitation energies are found to be strongly enhanced in the heavy fragment, if the level density follows a constant-temperature behavior at low energies, e.g., in the composed Gilbert-Cameron description.« less

REACT: Resettable Hold Down and Release Actuator for Space Applications

NASA Astrophysics Data System (ADS)

Nava, Nestor; Collado, Marcelo; Cabás, Ramiro

2014-07-01

A new HDRA based on SMA technology, called REACT, has been designed for development of loads and appendixes in space applications. This design involves a rod supported by spheres that block its axial movement during a preload application. The rod shape allows misalignment and blocks the rotation around axial axis for a proper installation of the device. Because of the high preload requirements for this type of actuators, finite element analysis (FEA) has been developed in order to check the structure resistance. The results of the FEA have constrained the REACT design, in terms of dimensions, materials, and shape of the mechanical parts. A complete test campaign for qualification of REACT is proposed. Several qualification models are intended to be built for testing in parallel. Therefore, it is a way to demonstrate margins which allows getting some statistics.

Android Malware Classification Using K-Means Clustering Algorithm

NASA Astrophysics Data System (ADS)

Hamid, Isredza Rahmi A.; Syafiqah Khalid, Nur; Azma Abdullah, Nurul; Rahman, Nurul Hidayah Ab; Chai Wen, Chuah

2017-08-01

Malware was designed to gain access or damage a computer system without user notice. Besides, attacker exploits malware to commit crime or fraud. This paper proposed Android malware classification approach based on K-Means clustering algorithm. We evaluate the proposed model in terms of accuracy using machine learning algorithms. Two datasets were selected to demonstrate the practicing of K-Means clustering algorithms that are Virus Total and Malgenome dataset. We classify the Android malware into three clusters which are ransomware, scareware and goodware. Nine features were considered for each types of dataset such as Lock Detected, Text Detected, Text Score, Encryption Detected, Threat, Porn, Law, Copyright and Moneypak. We used IBM SPSS Statistic software for data classification and WEKA tools to evaluate the built cluster. The proposed K-Means clustering algorithm shows promising result with high accuracy when tested using Random Forest algorithm.

A Survey of Models and Algorithms for Social Influence Analysis

NASA Astrophysics Data System (ADS)

Sun, Jimeng; Tang, Jie

Social influence is the behavioral change of a person because of the perceived relationship with other people, organizations and society in general. Social influence has been a widely accepted phenomenon in social networks for decades. Many applications have been built based around the implicit notation of social influence between people, such as marketing, advertisement and recommendations. With the exponential growth of online social network services such as Facebook and Twitter, social influence can for the first time be measured over a large population. In this chapter, we survey the research on social influence analysis with a focus on the computational aspects. First, we present statistical measurements related to social influence. Second, we describe the literature on social similarity and influences. Third, we present the research on social influence maximization which has many practical applications including marketing and advertisement.

Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics

NASA Astrophysics Data System (ADS)

Folkert, Michael R.; Setton, Jeremy; Apte, Aditya P.; Grkovski, Milan; Young, Robert J.; Schöder, Heiko; Thorstad, Wade L.; Lee, Nancy Y.; Deasy, Joseph O.; Oh, Jung Hun

2017-07-01

In this study, we investigate the use of imaging feature-based outcomes research (‘radiomics’) combined with machine learning techniques to develop robust predictive models for the risk of all-cause mortality (ACM), local failure (LF), and distant metastasis (DM) following definitive chemoradiation therapy (CRT). One hundred seventy four patients with stage III-IV oropharyngeal cancer (OC) treated at our institution with CRT with retrievable pre- and post-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans were identified. From pre-treatment PET scans, 24 representative imaging features of FDG-avid disease regions were extracted. Using machine learning-based feature selection methods, multiparameter logistic regression models were built incorporating clinical factors and imaging features. All model building methods were tested by cross validation to avoid overfitting, and final outcome models were validated on an independent dataset from a collaborating institution. Multiparameter models were statistically significant on 5 fold cross validation with the area under the receiver operating characteristic curve (AUC)  =  0.65 (p  =  0.004), 0.73 (p  =  0.026), and 0.66 (p  =  0.015) for ACM, LF, and DM, respectively. The model for LF retained significance on the independent validation cohort with AUC  =  0.68 (p  =  0.029) whereas the models for ACM and DM did not reach statistical significance, but resulted in comparable predictive power to the 5 fold cross validation with AUC  =  0.60 (p  =  0.092) and 0.65 (p  =  0.062), respectively. In the largest study of its kind to date, predictive features including increasing metabolic tumor volume, increasing image heterogeneity, and increasing tumor surface irregularity significantly correlated to mortality, LF, and DM on 5 fold cross validation in a relatively uniform single-institution cohort. The LF model also retained significance in an independent population.

Using exploratory regression to identify optimal driving factors for cellular automaton modeling of land use change.

PubMed

Feng, Yongjiu; Tong, Xiaohua

2017-09-22

Defining transition rules is an important issue in cellular automaton (CA)-based land use modeling because these models incorporate highly correlated driving factors. Multicollinearity among correlated driving factors may produce negative effects that must be eliminated from the modeling. Using exploratory regression under pre-defined criteria, we identified all possible combinations of factors from the candidate factors affecting land use change. Three combinations that incorporate five driving factors meeting pre-defined criteria were assessed. With the selected combinations of factors, three logistic regression-based CA models were built to simulate dynamic land use change in Shanghai, China, from 2000 to 2015. For comparative purposes, a CA model with all candidate factors was also applied to simulate the land use change. Simulations using three CA models with multicollinearity eliminated performed better (with accuracy improvements about 3.6%) than the model incorporating all candidate factors. Our results showed that not all candidate factors are necessary for accurate CA modeling and the simulations were not sensitive to changes in statistically non-significant driving factors. We conclude that exploratory regression is an effective method to search for the optimal combinations of driving factors, leading to better land use change models that are devoid of multicollinearity. We suggest identification of dominant factors and elimination of multicollinearity before building land change models, making it possible to simulate more realistic outcomes.

Complex traffic flow that allows as well as hampers lane-changing intrinsically contains social-dilemma structures

NASA Astrophysics Data System (ADS)

Iwamura, Yoshiro; Tanimoto, Jun

2018-02-01

To investigate an interesting question as to whether or not social dilemma structures can be found in a realistic traffic flow reproduced by a model, we built a new microscopic model in which an intentional driver may try lane-changing to go in front of other vehicles and may hamper others’ lane-changes. Our model consists of twofold parts; cellular automaton emulating a real traffic flow and evolutionary game theory to implement a driver’s decision making-process. Numerical results reveal that a social dilemma like the multi-player chicken game or prisoner’s dilemma game emerges depending on the traffic phase. This finding implies that a social dilemma, which has been investigated by applied mathematics so far, hides behind a traffic flow, which has been explored by fluid dynamics. Highlight - Complex system of traffic flow with consideration of driver’s decision making process is concerned. - A new model dovetailing cellular automaton with game theory is established. - Statistical result from numerical simulations reveals a social dilemma structure underlying traffic flow. - The social dilemma is triggered by a driver’s egocentric actions of lane-changing and hampering other’s lane-change.

Computational Process Modeling for Additive Manufacturing (OSU)

NASA Technical Reports Server (NTRS)

Bagg, Stacey; Zhang, Wei

2015-01-01

Powder-Bed Additive Manufacturing (AM) through Direct Metal Laser Sintering (DMLS) or Selective Laser Melting (SLM) is being used by NASA and the Aerospace industry to "print" parts that traditionally are very complex, high cost, or long schedule lead items. The process spreads a thin layer of metal powder over a build platform, then melts the powder in a series of welds in a desired shape. The next layer of powder is applied, and the process is repeated until layer-by-layer, a very complex part can be built. This reduces cost and schedule by eliminating very complex tooling and processes traditionally used in aerospace component manufacturing. To use the process to print end-use items, NASA seeks to understand SLM material well enough to develop a method of qualifying parts for space flight operation. Traditionally, a new material process takes many years and high investment to generate statistical databases and experiential knowledge, but computational modeling can truncate the schedule and cost -many experiments can be run quickly in a model, which would take years and a high material cost to run empirically. This project seeks to optimize material build parameters with reduced time and cost through modeling.

Sampling and sensitivity analyses tools (SaSAT) for computational modelling

PubMed Central

Hoare, Alexander; Regan, David G; Wilson, David P

2008-01-01

SaSAT (Sampling and Sensitivity Analysis Tools) is a user-friendly software package for applying uncertainty and sensitivity analyses to mathematical and computational models of arbitrary complexity and context. The toolbox is built in Matlab®, a numerical mathematical software package, and utilises algorithms contained in the Matlab® Statistics Toolbox. However, Matlab® is not required to use SaSAT as the software package is provided as an executable file with all the necessary supplementary files. The SaSAT package is also designed to work seamlessly with Microsoft Excel but no functionality is forfeited if that software is not available. A comprehensive suite of tools is provided to enable the following tasks to be easily performed: efficient and equitable sampling of parameter space by various methodologies; calculation of correlation coefficients; regression analysis; factor prioritisation; and graphical output of results, including response surfaces, tornado plots, and scatterplots. Use of SaSAT is exemplified by application to a simple epidemic model. To our knowledge, a number of the methods available in SaSAT for performing sensitivity analyses have not previously been used in epidemiological modelling and their usefulness in this context is demonstrated. PMID:18304361

Application of Multivariable Analysis and FTIR-ATR Spectroscopy to the Prediction of Properties in Campeche Honey

PubMed Central

Pat, Lucio; Ali, Bassam; Guerrero, Armando; Córdova, Atl V.; Garduza, José P.

2016-01-01

Attenuated total reflectance-Fourier transform infrared spectrometry and chemometrics model was used for determination of physicochemical properties (pH, redox potential, free acidity, electrical conductivity, moisture, total soluble solids (TSS), ash, and HMF) in honey samples. The reference values of 189 honey samples of different botanical origin were determined using Association Official Analytical Chemists, (AOAC), 1990; Codex Alimentarius, 2001, International Honey Commission, 2002, methods. Multivariate calibration models were built using partial least squares (PLS) for the measurands studied. The developed models were validated using cross-validation and external validation; several statistical parameters were obtained to determine the robustness of the calibration models: (PCs) optimum number of components principal, (SECV) standard error of cross-validation, (R 2 cal) coefficient of determination of cross-validation, (SEP) standard error of validation, and (R 2 val) coefficient of determination for external validation and coefficient of variation (CV). The prediction accuracy for pH, redox potential, electrical conductivity, moisture, TSS, and ash was good, while for free acidity and HMF it was poor. The results demonstrate that attenuated total reflectance-Fourier transform infrared spectrometry is a valuable, rapid, and nondestructive tool for the quantification of physicochemical properties of honey. PMID:28070445

Structure-based predictions of 13C-NMR chemical shifts for a series of 2-functionalized 5-(methylsulfonyl)-1-phenyl-1H-indoles derivatives using GA-based MLR method

NASA Astrophysics Data System (ADS)

Ghavami, Raouf; Sadeghi, Faridoon; Rasouli, Zolikha; Djannati, Farhad

2012-12-01

Experimental values for the 13C NMR chemical shifts (ppm, TMS = 0) at 300 K ranging from 96.28 ppm (C4' of indole derivative 17) to 159.93 ppm (C4' of indole derivative 23) relative to deuteride chloroform (CDCl3, 77.0 ppm) or dimethylsulfoxide (DMSO, 39.50 ppm) as internal reference in CDCl3 or DMSO-d6 solutions have been collected from literature for thirty 2-functionalized 5-(methylsulfonyl)-1-phenyl-1H-indole derivatives containing different substituted groups. An effective quantitative structure-property relationship (QSPR) models were built using hybrid method combining genetic algorithm (GA) based on stepwise selection multiple linear regression (SWS-MLR) as feature-selection tools and correlation models between each carbon atom of indole derivative and calculated descriptors. Each compound was depicted by molecular structural descriptors that encode constitutional, topological, geometrical, electrostatic, and quantum chemical features. The accuracy of all developed models were confirmed using different types of internal and external procedures and various statistical tests. Furthermore, the domain of applicability for each model which indicates the area of reliable predictions was defined.

A unified framework for image retrieval using keyword and visual features.

PubMed

Jing, Feng; Li, Mingling; Zhang, Hong-Jiang; Zhang, Bo

2005-07-01

In this paper, a unified image retrieval framework based on both keyword annotations and visual features is proposed. In this framework, a set of statistical models are built based on visual features of a small set of manually labeled images to represent semantic concepts and used to propagate keywords to other unlabeled images. These models are updated periodically when more images implicitly labeled by users become available through relevance feedback. In this sense, the keyword models serve the function of accumulation and memorization of knowledge learned from user-provided relevance feedback. Furthermore, two sets of effective and efficient similarity measures and relevance feedback schemes are proposed for query by keyword scenario and query by image example scenario, respectively. Keyword models are combined with visual features in these schemes. In particular, a new, entropy-based active learning strategy is introduced to improve the efficiency of relevance feedback for query by keyword. Furthermore, a new algorithm is proposed to estimate the keyword features of the search concept for query by image example. It is shown to be more appropriate than two existing relevance feedback algorithms. Experimental results demonstrate the effectiveness of the proposed framework.

Home noninvasive positive pressure ventilation with built-in software in stable hypercapnic COPD: a short-term prospective, multicenter, randomized, controlled trial.

PubMed

Zhou, Luqian; Li, Xiaoying; Guan, Lili; Chen, Jianhua; Guo, Bingpeng; Wu, Weiliang; Huo, Yating; Zhou, Ziqing; Liang, Zhenyu; Zhou, Yuqi; Tan, Jie; Chen, Xin; Song, Yuanlin; Chen, Rongchang

2017-01-01

The benefits of noninvasive positive pressure ventilation (NPPV) in patients with hypercapnic COPD are controversial. It is presumed that methodology and appropriate use of NIV ventilator might be crucial for the outcomes. With the new built-in software, the performance of NIV can be monitored at home, which can guarantee the compliance and appropriate use. This study investigated effects of home use of NIV in hypercapnia in COPD patients using the NIV ventilator with built-in software for monitoring. The current multicenter prospective, randomized, controlled trial enrolled patients with stable GOLD stages III and IV hypercapnic COPD. Patients were randomly assigned via a computer-generated randomization sequence, with a block size of four patients, to continue optimized treatment (control group) or to receive additional NPPV (intervention group) for 3 months. The primary outcome was arterial carbon dioxide pressure (PaCO 2 ). Data were derived from built-in software and analyzed every 4 weeks. Analysis was carried out with the intention to treat. This study is registered with ClinicalTrials.gov, number NCT02499718. Patients were recruited from 20 respiratory units in China from October 1, 2015, and recruitment was terminated with a record of the vital statistics on May 31, 2016. A total of 115 patients were randomly assigned to the NPPV group (n=57) or the control group (n=58). Patients complied well with NPPV therapy (mean [± standard deviation] day use 5.6±1.4 h). The mean estimation of leaks was 37.99±13.71 L/min. The changes in PaCO 2 (-10.41±0.97 vs -4.32±0.68 mmHg, P =0.03) and 6-min walk distance (6MWD) (38.2% vs 18.2%, P =0.02) were statistically significant in the NPPV group versus the control group. COPD assessment test (CAT) showed a positive trend ( P =0.06) in favor of the NPPV group. Pulmonary function and dyspnea were not different between groups. Ventilators equipped with built-in software provided methodology for monitoring NIV use at home, which could facilitate the improvement of compliance and quality control of NIV use. It was shown that three months use of NIV at home could reduce the PaCO 2 and improve exercise tolerance (6MWD) in chronic hypercapnic COPD patients.

Local air gap thickness and contact area models for realistic simulation of human thermo-physiological response

NASA Astrophysics Data System (ADS)

Psikuta, Agnes; Mert, Emel; Annaheim, Simon; Rossi, René M.

2018-02-01

To evaluate the quality of new energy-saving and performance-supporting building and urban settings, the thermal sensation and comfort models are often used. The accuracy of these models is related to accurate prediction of the human thermo-physiological response that, in turn, is highly sensitive to the local effect of clothing. This study aimed at the development of an empirical regression model of the air gap thickness and the contact area in clothing to accurately simulate human thermal and perceptual response. The statistical model predicted reliably both parameters for 14 body regions based on the clothing ease allowances. The effect of the standard error in air gap prediction on the thermo-physiological response was lower than the differences between healthy humans. It was demonstrated that currently used assumptions and methods for determination of the air gap thickness can produce a substantial error for all global, mean, and local physiological parameters, and hence, lead to false estimation of the resultant physiological state of the human body, thermal sensation, and comfort. Thus, this model may help researchers to strive for improvement of human thermal comfort, health, productivity, safety, and overall sense of well-being with simultaneous reduction of energy consumption and costs in built environment.

A Synoptic Weather Typing Approach to Assess Climate Change Impacts on Meteorological and Hydrological Risks at Local Scale in South-Central Canada

NASA Astrophysics Data System (ADS)

Cheng, Chad Shouquan; Li, Qian; Li, Guilong

2010-05-01

The synoptic weather typing approach has become popular in evaluating the impacts of climate change on a variety of environmental problems. One of the reasons is its ability to categorize a complex set of meteorological variables as a coherent index, which can facilitate analyses of local climate change impacts. The weather typing method has been applied in Environment Canada to analyze climatic change impacts on various meteorological/hydrological risks, such as freezing rain, heavy rainfall, high-/low-flow events, air pollution, and human health. These studies comprise of three major parts: (1) historical simulation modeling to verify the hazardous events, (2) statistical downscaling to provide station-scale future climate information, and (3) estimates of changes in frequency and magnitude of future hazardous meteorological/hydrological events in this century. To achieve these goals, in addition to synoptic weather typing, the modeling conceptualizations in meteorology and hydrology and various linear/nonlinear regression techniques were applied. Furthermore, a formal model result verification process has been built into the entire modeling exercise. The results of the verification, based on historical observations of the outcome variables predicted by the models, showed very good agreement. This paper will briefly summarize these research projects, focusing on the modeling exercise and results.

Development of a 3D Underground Cadastral System with Indoor Mapping for As-Built BIM: The Case Study of Gangnam Subway Station in Korea.

PubMed

Kim, Sangmin; Kim, Jeonghyun; Jung, Jaehoon; Heo, Joon

2015-12-09

The cadastral system provides land ownership information by registering and representing land boundaries on a map. The current cadastral system in Korea, however, focuses mainly on the management of 2D land-surface boundaries. It is not yet possible to provide efficient or reliable land administration, as this 2D system cannot support or manage land information on 3D properties (including architectures and civil infrastructures) for both above-ground and underground facilities. A geometrical model of the 3D parcel, therefore, is required for registration of 3D properties. This paper, considering the role of the cadastral system, proposes a framework for a 3D underground cadastral system that can register various types of 3D underground properties using indoor mapping for as-built Building Information Modeling (BIM). The implementation consists of four phases: (1) geometric modeling of a real underground infrastructure using terrestrial laser scanning data; (2) implementation of as-built BIM based on geometric modeling results; (3) accuracy assessment for created as-built BIM using reference points acquired by total station; and (4) creation of three types of 3D underground cadastral map to represent underground properties. The experimental results, based on indoor mapping for as-built BIM, show that the proposed framework for a 3D underground cadastral system is able to register the rights, responsibilities, and restrictions corresponding to the 3D underground properties. In this way, clearly identifying the underground physical situation enables more reliable and effective decision-making in all aspects of the national land administration system.

Microbiomes of the dust particles collected from the International Space Station and Spacecraft Assembly Facilities.

PubMed

Checinska, Aleksandra; Probst, Alexander J; Vaishampayan, Parag; White, James R; Kumar, Deepika; Stepanov, Victor G; Fox, George E; Nilsson, Henrik R; Pierson, Duane L; Perry, Jay; Venkateswaran, Kasthuri

2015-10-27

The International Space Station (ISS) is a unique built environment due to the effects of microgravity, space radiation, elevated carbon dioxide levels, and especially continuous human habitation. Understanding the composition of the ISS microbial community will facilitate further development of safety and maintenance practices. The primary goal of this study was to characterize the viable microbiome of the ISS-built environment. A second objective was to determine if the built environments of Earth-based cleanrooms associated with space exploration are an appropriate model of the ISS environment. Samples collected from the ISS and two cleanrooms at the Jet Propulsion Laboratory (JPL, Pasadena, CA) were analyzed by traditional cultivation, adenosine triphosphate (ATP), and propidium monoazide-quantitative polymerase chain reaction (PMA-qPCR) assays to estimate viable microbial populations. The 16S rRNA gene Illumina iTag sequencing was used to elucidate microbial diversity and explore differences between ISS and cleanroom microbiomes. Statistical analyses showed that members of the phyla Actinobacteria, Firmicutes, and Proteobacteria were dominant in the samples examined but varied in abundance. Actinobacteria were predominant in the ISS samples whereas Proteobacteria, least abundant in the ISS, dominated in the cleanroom samples. The viable bacterial populations seen by PMA treatment were greatly decreased. However, the treatment did not appear to have an effect on the bacterial composition (diversity) associated with each sampling site. The results of this study provide strong evidence that specific human skin-associated microorganisms make a substantial contribution to the ISS microbiome, which is not the case in Earth-based cleanrooms. For example, Corynebacterium and Propionibacterium (Actinobacteria) but not Staphylococcus (Firmicutes) species are dominant on the ISS in terms of viable and total bacterial community composition. The results obtained will facilitate future studies to determine how stable the ISS environment is over time. The present results also demonstrate the value of measuring viable cell diversity and population size at any sampling site. This information can be used to identify sites that can be targeted for more stringent cleaning. Finally, the results will allow comparisons with other built sites and facilitate future improvements on the ISS that will ensure astronaut health.

The Built Environment and Active Travel: Evidence from Nanjing, China.

PubMed

Feng, Jianxi

2016-03-08

An established relationship exists between the built environment and active travel. Nevertheless, the literature examining the impacts of different components of the built environment is limited. In addition, most existing studies are based on data from cities in the U.S. and Western Europe. The situation in Chinese cities remains largely unknown. Based on data from Nanjing, China, this study explicitly examines the influences of two components of the built environment--the neighborhood form and street form--on residents' active travel. Binary logistic regression analyses examined the effects of the neighborhood form and street form on subsistence, maintenance and discretionary travel, respectively. For each travel purpose, three models are explored: a model with only socio-demographics, a model with variables of the neighborhood form and a complete model with all variables. The model fit indicator, Nagelkerke's ρ², increased by 0.024 when neighborhood form variables are included and increased by 0.070 when street form variables are taken into account. A similar situation can be found in the models of maintenance activities and discretionary activities. Regarding specific variables, very limited significant impacts of the neighborhood form variables are observed, while almost all of the characteristics of the street form show significant influences on active transport. In Nanjing, street form factors have a more profound influence on active travel than neighborhood form factors. The focal point of the land use regulations and policy of local governments should shift from the neighborhood form to the street form to maximize the effects of policy interventions.

Physics-Based Image Segmentation Using First Order Statistical Properties and Genetic Algorithm for Inductive Thermography Imaging.

PubMed

Gao, Bin; Li, Xiaoqing; Woo, Wai Lok; Tian, Gui Yun

2018-05-01

Thermographic inspection has been widely applied to non-destructive testing and evaluation with the capabilities of rapid, contactless, and large surface area detection. Image segmentation is considered essential for identifying and sizing defects. To attain a high-level performance, specific physics-based models that describe defects generation and enable the precise extraction of target region are of crucial importance. In this paper, an effective genetic first-order statistical image segmentation algorithm is proposed for quantitative crack detection. The proposed method automatically extracts valuable spatial-temporal patterns from unsupervised feature extraction algorithm and avoids a range of issues associated with human intervention in laborious manual selection of specific thermal video frames for processing. An internal genetic functionality is built into the proposed algorithm to automatically control the segmentation threshold to render enhanced accuracy in sizing the cracks. Eddy current pulsed thermography will be implemented as a platform to demonstrate surface crack detection. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. In addition, a global quantitative assessment index F-score has been adopted to objectively evaluate the performance of different segmentation algorithms.

Prediction of high-frequency vibration transmission across coupled, periodic ribbed plates by incorporating tunneling mechanisms.

PubMed

Yin, Jianfei; Hopkins, Carl

2013-04-01

Prediction of structure-borne sound transmission on built-up structures at audio frequencies is well-suited to Statistical Energy Analysis (SEA) although the inclusion of periodic ribbed plates presents challenges. This paper considers an approach using Advanced SEA (ASEA) that can incorporate tunneling mechanisms within a statistical approach. The coupled plates used for the investigation form an L-junction comprising a periodic ribbed plate with symmetric ribs and an isotropic homogeneous plate. Experimental SEA (ESEA) is carried out with input data from Finite Element Methods (FEM). This indicates that indirect coupling is significant at high frequencies where bays on the periodic ribbed plate can be treated as individual subsystems. SEA using coupling loss factors from wave theory leads to significant underestimates in the energy of the bays when the isotropic homogeneous plate is excited. This is due to the absence of tunneling mechanisms in the SEA model. In contrast, ASEA shows close agreement with FEM and laboratory measurements. The errors incurred with SEA rapidly increase as the bays become more distant from the source subsystem. ASEA provides significantly more accurate predictions by accounting for the spatial filtering that leads to non-diffuse vibration fields on these more distant bays.

Automatic recognition of surface landmarks of anatomical structures of back and posture

NASA Astrophysics Data System (ADS)

Michoński, Jakub; Glinkowski, Wojciech; Witkowski, Marcin; Sitnik, Robert

2012-05-01

Faulty postures, scoliosis and sagittal plane deformities should be detected as early as possible to apply preventive and treatment measures against major clinical consequences. To support documentation of the severity of deformity and diminish x-ray exposures, several solutions utilizing analysis of back surface topography data were introduced. A novel approach to automatic recognition and localization of anatomical landmarks of the human back is presented that may provide more repeatable results and speed up the whole procedure. The algorithm was designed as a two-step process involving a statistical model built upon expert knowledge and analysis of three-dimensional back surface shape data. Voronoi diagram is used to connect mean geometric relations, which provide a first approximation of the positions, with surface curvature distribution, which further guides the recognition process and gives final locations of landmarks. Positions obtained using the developed algorithms are validated with respect to accuracy of manual landmark indication by experts. Preliminary validation proved that the landmarks were localized correctly, with accuracy depending mostly on the characteristics of a given structure. It was concluded that recognition should mainly take into account the shape of the back surface, putting as little emphasis on the statistical approximation as possible.

Applying the Longitudinal Model from Item Response Theory to Assess Health-Related Quality of Life in the PRODIGE 4/ACCORD 11 Randomized Trial.

PubMed

Barbieri, Antoine; Anota, Amélie; Conroy, Thierry; Gourgou-Bourgade, Sophie; Juzyna, Beata; Bonnetain, Franck; Lavergne, Christian; Bascoul-Mollevi, Caroline

2016-07-01

A new longitudinal statistical approach was compared to the classical methods currently used to analyze health-related quality-of-life (HRQoL) data. The comparison was made using data in patients with metastatic pancreatic cancer. Three hundred forty-two patients from the PRODIGE4/ACCORD 11 study were randomly assigned to FOLFIRINOX versus gemcitabine regimens. HRQoL was evaluated using the European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30. The classical analysis uses a linear mixed model (LMM), considering an HRQoL score as a good representation of the true value of the HRQoL, following EORTC recommendations. In contrast, built on the item response theory (IRT), our approach considered HRQoL as a latent variable directly estimated from the raw data. For polytomous items, we extended the partial credit model to a longitudinal analysis (longitudinal partial credit model [LPCM]), thereby modeling the latent trait as a function of time and other covariates. Both models gave the same conclusions on 11 of 15 HRQoL dimensions. HRQoL evolution was similar between the 2 treatment arms, except for the symptoms of pain. Indeed, regarding the LPCM, pain perception was significantly less important in the FOLFIRINOX arm than in the gemcitabine arm. For most of the scales, HRQoL changes over time, and no difference was found between treatments in terms of HRQoL. The use of LMM to study the HRQoL score does not seem appropriate. It is an easy-to-use model, but the basic statistical assumptions do not check. Our IRT model may be more complex but shows the same qualities and gives similar results. It has the additional advantage of being more precise and suitable because of its direct use of raw data. © The Author(s) 2015.

Built-up Land Expansion in Urban China

NASA Astrophysics Data System (ADS)

Chen, Yi; Chen, Zhigang; Huang, Xianjin

2017-04-01

Since the implementation of the reform and opening-up, rapid expansion of built-up land has caused a rapid reduction of arable land. The Ministry of Land and Resources of the People' s Republic of China has strengthened the management of built-up land through the basic arable land protection and the quota allocation of built-up land to control the urban sprawl. In addition, the general land use planning and the annual land use plan have been used to further ensure the effectiveness of land use management and control. However, the trend of built-up land expansion has not been effectively restrained. The built-up land expansion increased from 31.92 × 106 hm2 in 2005 to 38.89 × 106 hm2 in 2012. The rapid expansion of built-up land has been the major feature of land use changes in China and has led to built-up land vacancy and inefficient land use. This paper used a Data Envelopment Analysis (DEA) model to analyze the changes in built-up land efficiency in 336 cities in China from 2005 to 2012 during the implementation of National General Land Use Plan (2006-2020) (NGLUP). The results showed that the built-up land input-output efficiency of most cities declined, and more than half of the cities had excessive inputs of built-up land. Even in the most developed region of China, the built-up land efficiency was relatively low. The paper argues that the NGLUP failed to control the expansion of built-up land and to promote intensive land use. The allocation of built-up land designated by the Plan was not reasonable, and economic development has greatly relied on land inputs, which need to be improved. The paper finally suggests that the built-up land indices should be appropriately directed toward economically underdeveloped regions in central and western China, and the establishment of a withdrawal mechanism for inefficient land would better promote the efficient allocation of built-up land.

Built environment and diabetes

PubMed Central

Pasala, Sudhir Kumar; Rao, Allam Appa; Sridhar, G. R.

2010-01-01

Development of type 2 diabetes mellitus is influenced by built environment, which is, ‘the environments that are modified by humans, including homes, schools, workplaces, highways, urban sprawls, accessibility to amenities, leisure, and pollution.’ Built environment contributes to diabetes through access to physical activity and through stress, by affecting the sleep cycle. With globalization, there is a possibility that western environmental models may be replicated in developing countries such as India, where the underlying genetic predisposition makes them particularly susceptible to diabetes. Here we review published information on the relationship between built environment and diabetes, so that appropriate modifications can be incorporated to reduce the risk of developing diabetes mellitus. PMID:20535308

Archaeology and astronomy

NASA Astrophysics Data System (ADS)

2009-10-01

MEETING REPORT The interaction between archaeology and astronomy has a long, tangled and not entirely creditable history, marred by misunderstandings on both sides. But statistics and cultural awareness are bringing a better picture of how and why lasting monuments such as Stonehenge were built. Sue Bowler reports on a joint meeting of the Royal Astronomical Society and the Prehistoric Society, held at Jodrell Bank on 17 July 2009.

The Elementary School Classroom. The Study of the Built Environment Through Student and Teacher Responses. The Elementary School and Its Population, Phase 2.

ERIC Educational Resources Information Center

Artinian, Vrej-Armen

An extensive investigation of elementary school classrooms was conducted through the collection and statistical analysis of student and teacher responses to questions concerning the educational environment. Several asepcts of the classroom are discussed, including the spatial, thermal, luminous, and aural environments. Questions were organized so…

Surface Impact Simulations of Helium Nanodroplets

DTIC Science & Technology

2015-06-30

mechanical delocalization of the individual helium atoms in the droplet and the quan- tum statistical effects that accompany the interchange of identical...incorporates the effects of atomic delocaliza- tion by treating individual atoms as smeared-out probability distributions that move along classical...probability density distributions to give effec- tive interatomic potential energy curves that have zero-point averaging effects built into them [25

Sub-Saharan Africa Report.

DTIC Science & Technology

1987-04-27

doctors, four nurses, and auxiliary personnel are being built nearby. Soviet medical specialists are assigned to the facility, but travel to Luanda...with various rehabilitation projects. In the medical field, the program recommends the immediate assignment of technical personnel, steps to promote...protection, food, shelter, clothing and medical attention. Statistical data from 1980 reveal that about 50 percent of the total population of Cabo

Understanding the Independent and Joint Associations of the Home and Workplace Built Environments on Cardiorespiratory Fitness and Body Mass Index

PubMed Central

Hoehner, Christine M.; Allen, Peg; Barlow, Carolyn E.; Marx, Christine M.; Brownson, Ross C.; Schootman, Mario

2013-01-01

This observational study examined the associations of built environment features around the home and workplace with cardiorespiratory fitness (CRF) based on a treadmill test and body mass index (BMI) (weight (kg)/height (m)2). The study included 8,857 adults aged 20–88 years who completed a preventive medical examination in 2000–2007 while living in 12 Texas counties. Analyses examining workplace neighborhood characteristics included a subset of 4,734 participants. Built environment variables were derived around addresses by using geographic information systems. Models were adjusted for individual-level and census block group–level demographics and socioeconomic status, smoking, BMI (in CRF models), and all other home or workplace built environment variables. CRF was associated with higher intersection density, higher number of private exercise facilities around the home and workplace, larger area of vegetation around the home, and shorter distance to the closest city center. Aside from vegetation, these same built environment features around the home were also associated with BMI. Participants who lived and worked in neighborhoods in the lowest tertiles for intersection density and the number of private exercise facilities had lower CRF and higher BMI values than participants who lived and worked in higher tertiles for these variables. This study contributes new evidence to suggest that built environment features around homes and workplaces may affect health. PMID:23942215

The built environment and alcohol consumption in urban neighborhoods.

PubMed

Bernstein, Kyle T; Galea, Sandro; Ahern, Jennifer; Tracy, Melissa; Vlahov, David

2007-12-01

To examine the relations between characteristics of the neighborhood built environment and recent alcohol use. We recruited participants through a random digit dial telephone survey of New York City (NYC) residents. Alcohol consumption was assessed using a structured interview. All respondents were assigned to neighborhood of residence. Data on the internal and external built environment in 59 NYC neighborhoods were collected from archival sources. Multilevel models were used to assess the adjusted relations between features of the built environment and alcohol use. Of the 1355 respondents, 40% reported any alcohol consumption in the past 30 days, and 3% reported more than five drinks in one sitting (heavy drinking) in the past 30 days. Few characteristics of the built environment were associated with any alcohol use in the past 30 days. However, several features of the internal and external built environment were associated with recent heavy drinking. After adjustment, persons living in neighborhoods characterized by poorer features of the built environment were up to 150% more likely to report heavy drinking in the last 30 days compared to persons living in neighborhoods characterized by a better built environment. Quality of the neighborhood built environment may be associated with heavy alcohol consumption in urban populations, independent of individual characteristics. The role of the residential environment as a determinant of alcohol abuse warrants further examination.

Estimating true human and animal host source contribution in quantitative microbial source tracking using the Monte Carlo method.

PubMed

Wang, Dan; Silkie, Sarah S; Nelson, Kara L; Wuertz, Stefan

2010-09-01

Cultivation- and library-independent, quantitative PCR-based methods have become the method of choice in microbial source tracking. However, these qPCR assays are not 100% specific and sensitive for the target sequence in their respective hosts' genome. The factors that can lead to false positive and false negative information in qPCR results are well defined. It is highly desirable to have a way of removing such false information to estimate the true concentration of host-specific genetic markers and help guide the interpretation of environmental monitoring studies. Here we propose a statistical model based on the Law of Total Probability to predict the true concentration of these markers. The distributions of the probabilities of obtaining false information are estimated from representative fecal samples of known origin. Measurement error is derived from the sample precision error of replicated qPCR reactions. Then, the Monte Carlo method is applied to sample from these distributions of probabilities and measurement error. The set of equations given by the Law of Total Probability allows one to calculate the distribution of true concentrations, from which their expected value, confidence interval and other statistical characteristics can be easily evaluated. The output distributions of predicted true concentrations can then be used as input to watershed-wide total maximum daily load determinations, quantitative microbial risk assessment and other environmental models. This model was validated by both statistical simulations and real world samples. It was able to correct the intrinsic false information associated with qPCR assays and output the distribution of true concentrations of Bacteroidales for each animal host group. Model performance was strongly affected by the precision error. It could perform reliably and precisely when the standard deviation of the precision error was small (≤ 0.1). Further improvement on the precision of sample processing and qPCR reaction would greatly improve the performance of the model. This methodology, built upon Bacteroidales assays, is readily transferable to any other microbial source indicator where a universal assay for fecal sources of that indicator exists. Copyright © 2010 Elsevier Ltd. All rights reserved.

Reporting quality of statistical methods in surgical observational studies: protocol for systematic review.

PubMed

Wu, Robert; Glen, Peter; Ramsay, Tim; Martel, Guillaume

2014-06-28

Observational studies dominate the surgical literature. Statistical adjustment is an important strategy to account for confounders in observational studies. Research has shown that published articles are often poor in statistical quality, which may jeopardize their conclusions. The Statistical Analyses and Methods in the Published Literature (SAMPL) guidelines have been published to help establish standards for statistical reporting.This study will seek to determine whether the quality of statistical adjustment and the reporting of these methods are adequate in surgical observational studies. We hypothesize that incomplete reporting will be found in all surgical observational studies, and that the quality and reporting of these methods will be of lower quality in surgical journals when compared with medical journals. Finally, this work will seek to identify predictors of high-quality reporting. This work will examine the top five general surgical and medical journals, based on a 5-year impact factor (2007-2012). All observational studies investigating an intervention related to an essential component area of general surgery (defined by the American Board of Surgery), with an exposure, outcome, and comparator, will be included in this systematic review. Essential elements related to statistical reporting and quality were extracted from the SAMPL guidelines and include domains such as intent of analysis, primary analysis, multiple comparisons, numbers and descriptive statistics, association and correlation analyses, linear regression, logistic regression, Cox proportional hazard analysis, analysis of variance, survival analysis, propensity analysis, and independent and correlated analyses. Each article will be scored as a proportion based on fulfilling criteria in relevant analyses used in the study. A logistic regression model will be built to identify variables associated with high-quality reporting. A comparison will be made between the scores of surgical observational studies published in medical versus surgical journals. Secondary outcomes will pertain to individual domains of analysis. Sensitivity analyses will be conducted. This study will explore the reporting and quality of statistical analyses in surgical observational studies published in the most referenced surgical and medical journals in 2013 and examine whether variables (including the type of journal) can predict high-quality reporting.

Zero inflation in ordinal data: Incorporating susceptibility to response through the use of a mixture model

PubMed Central

Kelley, Mary E.; Anderson, Stewart J.

2008-01-01

Summary The aim of the paper is to produce a methodology that will allow users of ordinal scale data to more accurately model the distribution of ordinal outcomes in which some subjects are susceptible to exhibiting the response and some are not (i.e., the dependent variable exhibits zero inflation). This situation occurs with ordinal scales in which there is an anchor that represents the absence of the symptom or activity, such as “none”, “never” or “normal”, and is particularly common when measuring abnormal behavior, symptoms, and side effects. Due to the unusually large number of zeros, traditional statistical tests of association can be non-informative. We propose a mixture model for ordinal data with a built-in probability of non-response that allows modeling of the range (e.g., severity) of the scale, while simultaneously modeling the presence/absence of the symptom. Simulations show that the model is well behaved and a likelihood ratio test can be used to choose between the zero-inflated and the traditional proportional odds model. The model, however, does have minor restrictions on the nature of the covariates that must be satisfied in order for the model to be identifiable. The method is particularly relevant for public health research such as large epidemiological surveys where more careful documentation of the reasons for response may be difficult. PMID:18351711

Anatomical modeling of the bronchial tree

NASA Astrophysics Data System (ADS)

Hentschel, Gerrit; Klinder, Tobias; Blaffert, Thomas; Bülow, Thomas; Wiemker, Rafael; Lorenz, Cristian

2010-02-01

The bronchial tree is of direct clinical importance in the context of respective diseases, such as chronic obstructive pulmonary disease (COPD). It furthermore constitutes a reference structure for object localization in the lungs and it finally provides access to lung tissue in, e.g., bronchoscope based procedures for diagnosis and therapy. This paper presents a comprehensive anatomical model for the bronchial tree, including statistics of position, relative and absolute orientation, length, and radius of 34 bronchial segments, going beyond previously published results. The model has been built from 16 manually annotated CT scans, covering several branching variants. The model is represented as a centerline/tree structure but can also be converted in a surface representation. Possible model applications are either to anatomically label extracted bronchial trees or to improve the tree extraction itself by identifying missing segments or sub-trees, e.g., if located beyond a bronchial stenosis. Bronchial tree labeling is achieved using a naïve Bayesian classifier based on the segment properties contained in the model in combination with tree matching. The tree matching step makes use of branching variations covered by the model. An evaluation of the model has been performed in a leaveone- out manner. In total, 87% of the branches resulting from preceding airway tree segmentation could be correctly labeled. The individualized model enables the detection of missing branches, allowing a targeted search, e.g., a local rerun of the tree-segmentation segmentation.

Environmental and personal correlates of physical activity and sedentary behavior in African American women: An ecological momentary assessment study.

PubMed

Zenk, Shannon N; Horoi, Irina; Jones, Kelly K; Finnegan, Lorna; Corte, Colleen; Riley, Barth; Wilbur, JoEllen

2017-04-01

The authors of this study examined within-person associations of environmental factors (weather, built and social environmental barriers) and personal factors (daily hassles, affect) with moderate-to-vigorous physical activity (MVPA) and sedentary behavior (SB) in African American women aged 25-64 years living in metropolitan Chicago (n = 97). In 2012-13, for seven days, women wore an accelerometer and were signaled five times per day to complete a survey covering environmental and personal factors on a study-provided smartphone. Day-level measures of each were derived, and mixed regression models were used to test associations. Poor weather was associated with a 27.3% reduction in daily MVPA. Associations between built and social environmental barriers and daily MVPA or SB were generally not statistically significant. Negative affect at the first daily signal was associated with a 38.6% decrease in subsequent daily MVPA and a 33.2-minute increase in subsequent daily SB. Each one-minute increase in MVPA during the day was associated with a 2.2% higher likelihood of positive affect at the end of the day. SB during the day was associated with lower subsequent positive affect. Real-time interventions that address overcoming poor weather and negative affect may help African American women increase MVPA and/or decrease SB.

An optimal set of landmarks for metopic craniosynostosis diagnosis from shape analysis of pediatric CT scans of the head

NASA Astrophysics Data System (ADS)

Mendoza, Carlos S.; Safdar, Nabile; Myers, Emmarie; Kittisarapong, Tanakorn; Rogers, Gary F.; Linguraru, Marius George

2013-02-01

Craniosynostosis (premature fusion of skull sutures) is a severe condition present in one of every 2000 newborns. Metopic craniosynostosis, accounting for 20-27% of cases, is diagnosed qualitatively in terms of skull shape abnormality, a subjective call of the surgeon. In this paper we introduce a new quantitative diagnostic feature for metopic craniosynostosis derived optimally from shape analysis of CT scans of the skull. We built a robust shape analysis pipeline that is capable of obtaining local shape differences in comparison to normal anatomy. Spatial normalization using 7-degree-of-freedom registration of the base of the skull is followed by a novel bone labeling strategy based on graph-cuts according to labeling priors. The statistical shape model built from 94 normal subjects allows matching a patient's anatomy to its most similar normal subject. Subsequently, the computation of local malformations from a normal subject allows characterization of the points of maximum malformation on each of the frontal bones adjacent to the metopic suture, and on the suture itself. Our results show that the malformations at these locations vary significantly (p<0.001) between abnormal/normal subjects and that an accurate diagnosis can be achieved using linear regression from these automatic measurements with an area under the curve for the receiver operating characteristic of 0.97.

Quasi-Static Probabilistic Structural Analyses Process and Criteria

NASA Technical Reports Server (NTRS)

Goldberg, B.; Verderaime, V.

1999-01-01

Current deterministic structural methods are easily applied to substructures and components, and analysts have built great design insights and confidence in them over the years. However, deterministic methods cannot support systems risk analyses, and it was recently reported that deterministic treatment of statistical data is inconsistent with error propagation laws that can result in unevenly conservative structural predictions. Assuming non-nal distributions and using statistical data formats throughout prevailing stress deterministic processes lead to a safety factor in statistical format, which integrated into the safety index, provides a safety factor and first order reliability relationship. The embedded safety factor in the safety index expression allows a historically based risk to be determined and verified over a variety of quasi-static metallic substructures consistent with the traditional safety factor methods and NASA Std. 5001 criteria.

Towards a Web-Enabled Geovisualization and Analytics Platform for the Energy and Water Nexus

NASA Astrophysics Data System (ADS)

Sanyal, J.; Chandola, V.; Sorokine, A.; Allen, M.; Berres, A.; Pang, H.; Karthik, R.; Nugent, P.; McManamay, R.; Stewart, R.; Bhaduri, B. L.

2017-12-01

Interactive data analytics are playing an increasingly vital role in the generation of new, critical insights regarding the complex dynamics of the energy/water nexus (EWN) and its interactions with climate variability and change. Integration of impacts, adaptation, and vulnerability (IAV) science with emerging, and increasingly critical, data science capabilities offers a promising potential to meet the needs of the EWN community. To enable the exploration of pertinent research questions, a web-based geospatial visualization platform is being built that integrates a data analysis toolbox with advanced data fusion and data visualization capabilities to create a knowledge discovery framework for the EWN. The system, when fully built out, will offer several geospatial visualization capabilities including statistical visual analytics, clustering, principal-component analysis, dynamic time warping, support uncertainty visualization and the exploration of data provenance, as well as support machine learning discoveries to render diverse types of geospatial data and facilitate interactive analysis. Key components in the system architecture includes NASA's WebWorldWind, the Globus toolkit, postgresql, as well as other custom built software modules.

Common mental disorders and the built environment in Santiago, Chile.

PubMed

Araya, Ricardo; Montgomery, Alan; Rojas, Graciela; Fritsch, Rosemarie; Solis, Jaime; Signorelli, Andres; Lewis, Glyn

2007-05-01

There is growing research interest in the influence of the built environment on mental disorders. To estimate the variation in the prevalence of common mental disorders attributable to individuals and the built environment of geographical sectors where they live. A sample of 3870 adults (response rate 90%) clustered in 248 geographical sectors participated in a household cross-sectional survey in Santiago, Chile. Independently rated contextual measures of the built environment were obtained. The Clinical Interview Schedule was used to estimate the prevalence of common mental disorders. There was a significant association between the quality of the built environment of small geographical sectors and the presence of common mental disorders among its residents. The better the quality of the built environment, the lower the scores for psychiatric symptoms; however, only a small proportion of the variation in common mental disorder existed at sector level, after adjusting for individual factors. Findings from our study, using a contextual assessment of the quality of the built environment and multilevel modelling in the analysis, suggest these associations may be more marked in non-Western settings with more homogeneous geographical sectors.

Spiritual and Affective Responses to a Physical Church and Corresponding Virtual Model.

PubMed

Murdoch, Matt; Davies, Jim

2017-11-01

Architectural and psychological theories posit that built environments have the potential to elicit complex psychological responses. However, few researchers have seriously explored this potential. Given the increasing importance and fidelity of virtual worlds, such research should explore whether virtual models of built environments are also capable of eliciting complex psychological responses. The goal of this study was to test these hypotheses, using a church, a corresponding virtual model, and an inclusive measure of state spirituality ("spiritual feelings"). Participants (n = 33) explored a physical church and corresponding virtual model, completing a measure of spiritual feelings after exploring the outside and inside of each version of the church. Using spiritual feelings after exploring the outside of the church as a baseline measure, change in state spirituality was assessed by taking the difference between spiritual feelings after exploring the inside and outside of the church (inside-outside) for both models. Although this change was greater in response to the physical church, there was no significant difference between the two models in eliciting such change in spiritual feelings. Despite the limitations of this exploratory study, these findings indicate that both built environments and corresponding virtual models are capable of evoking complex psychological responses.

Flare parameters inferred from a 3D loop model data base

NASA Astrophysics Data System (ADS)

Cuambe, Valente A.; Costa, J. E. R.; Simões, P. J. A.

2018-06-01

We developed a data base of pre-calculated flare images and spectra exploring a set of parameters which describe the physical characteristics of coronal loops and accelerated electron distribution. Due to the large number of parameters involved in describing the geometry and the flaring atmosphere in the model used, we built a large data base of models (˜250 000) to facilitate the flare analysis. The geometry and characteristics of non-thermal electrons are defined on a discrete grid with spatial resolution greater than 4 arcsec. The data base was constructed based on general properties of known solar flares and convolved with instrumental resolution to replicate the observations from the Nobeyama radio polarimeter spectra and Nobeyama radioheliograph (NoRH) brightness maps. Observed spectra and brightness distribution maps are easily compared with the modelled spectra and images in the data base, indicating a possible range of solutions. The parameter search efficiency in this finite data base is discussed. 8 out of 10 parameters analysed for 1000 simulated flare searches were recovered with a relative error of less than 20 per cent on average. In addition, from the analysis of the observed correlation between NoRH flare sizes and intensities at 17 GHz, some statistical properties were derived. From these statistics, the energy spectral index was found to be δ ˜ 3, with non-thermal electron densities showing a peak distribution ⪅107 cm-3, and Bphotosphere ⪆ 2000 G. Some bias for larger loops with heights as great as ˜2.6 × 109 cm, and looptop events were noted. An excellent match of the spectrum and the brightness distribution at 17 and 34 GHz of the 2002 May 31 flare is presented as well.

Using active shape modeling based on MRI to study morphologic and pitch-related functional changes affecting vocal structures and the airway.

PubMed

Miller, Nicola A; Gregory, Jennifer S; Aspden, Richard M; Stollery, Peter J; Gilbert, Fiona J

2014-09-01

The shape of the vocal tract and associated structures (eg, tongue and velum) is complicated and varies according to development and function. This variability challenges interpretation of voice experiments. Quantifying differences between shapes and understanding how vocal structures move in relation to each other is difficult using traditional linear and angle measurements. With statistical shape models, shape can be characterized in terms of independent modes of variation. Here, we build an active shape model (ASM) to assess morphologic and pitch-related functional changes affecting vocal structures and the airway. Using a cross-sectional study design, we obtained six midsagittal magnetic resonance images from 10 healthy adults (five men and five women) at rest, while breathing out, and while listening to, and humming low and high notes. Eighty landmark points were chosen to define the shape of interest and an ASM was built using these (60) images. Principal component analysis was used to identify independent modes of variation, and statistical analysis was performed using one-way repeated-measures analysis of variance. Twenty modes of variation were identified with modes 1 and 2 accounting for half the total variance. Modes 1 and 9 were significantly associated with humming low and high notes (P < 0.001) and showed coordinated changes affecting the cervical spine, vocal structures, and airway. Mode 2 highlighted wide structural variations between subjects. This study highlights the potential of active shape modeling to advance understanding of factors underlying morphologic and pitch-related functional variations affecting vocal structures and the airway in health and disease. Copyright © 2014 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Statistical sex determination from craniometrics: Comparison of linear discriminant analysis, logistic regression, and support vector machines.

PubMed

Santos, Frédéric; Guyomarc'h, Pierre; Bruzek, Jaroslav

2014-12-01

Accuracy of identification tools in forensic anthropology primarily rely upon the variations inherent in the data upon which they are built. Sex determination methods based on craniometrics are widely used and known to be specific to several factors (e.g. sample distribution, population, age, secular trends, measurement technique, etc.). The goal of this study is to discuss the potential variations linked to the statistical treatment of the data. Traditional craniometrics of four samples extracted from documented osteological collections (from Portugal, France, the U.S.A., and Thailand) were used to test three different classification methods: linear discriminant analysis (LDA), logistic regression (LR), and support vector machines (SVM). The Portuguese sample was set as a training model on which the other samples were applied in order to assess the validity and reliability of the different models. The tests were performed using different parameters: some included the selection of the best predictors; some included a strict decision threshold (sex assessed only if the related posterior probability was high, including the notion of indeterminate result); and some used an unbalanced sex-ratio. Results indicated that LR tends to perform slightly better than the other techniques and offers a better selection of predictors. Also, the use of a decision threshold (i.e. p>0.95) is essential to ensure an acceptable reliability of sex determination methods based on craniometrics. Although the Portuguese, French, and American samples share a similar sexual dimorphism, application of Western models on the Thai sample (that displayed a lower degree of dimorphism) was unsuccessful. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

An advanced simulator for orthopedic surgical training.

PubMed

Cecil, J; Gupta, Avinash; Pirela-Cruz, Miguel

2018-02-01

The purpose of creating the virtual reality (VR) simulator is to facilitate and supplement the training opportunities provided to orthopedic residents. The use of VR simulators has increased rapidly in the field of medical surgery for training purposes. This paper discusses the creation of the virtual surgical environment (VSE) for training residents in an orthopedic surgical process called less invasive stabilization system (LISS) surgery which is used to address fractures of the femur. The overall methodology included first obtaining an understanding of the LISS plating process through interactions with expert orthopedic surgeons and developing the information centric models. The information centric models provided a structured basis to design and build the simulator. Subsequently, the haptic-based simulator was built. Finally, the learning assessments were conducted in a medical school. The results from the learning assessments confirm the effectiveness of the VSE for teaching medical residents and students. The scope of the assessment was to ensure (1) the correctness and (2) the usefulness of the VSE. Out of 37 residents/students who participated in the test, 32 showed improvements in their understanding of the LISS plating surgical process. A majority of participants were satisfied with the use of teaching Avatars and haptic technology. A paired t test was conducted to test the statistical significance of the assessment data which showed that the data were statistically significant. This paper demonstrates the usefulness of adopting information centric modeling approach in the design and development of the simulator. The assessment results underscore the potential of using VR-based simulators in medical education especially in orthopedic surgery.

Detection of Single Standing Dead Trees from Aerial Color Infrared Imagery by Segmentation with Shape and Intensity Priors

NASA Astrophysics Data System (ADS)

Polewski, P.; Yao, W.; Heurich, M.; Krzystek, P.; Stilla, U.

2015-03-01

Standing dead trees, known as snags, are an essential factor in maintaining biodiversity in forest ecosystems. Combined with their role as carbon sinks, this makes for a compelling reason to study their spatial distribution. This paper presents an integrated method to detect and delineate individual dead tree crowns from color infrared aerial imagery. Our approach consists of two steps which incorporate statistical information about prior distributions of both the image intensities and the shapes of the target objects. In the first step, we perform a Gaussian Mixture Model clustering in the pixel color space with priors on the cluster means, obtaining up to 3 components corresponding to dead trees, living trees, and shadows. We then refine the dead tree regions using a level set segmentation method enriched with a generative model of the dead trees' shape distribution as well as a discriminative model of their pixel intensity distribution. The iterative application of the statistical shape template yields the set of delineated dead crowns. The prior information enforces the consistency of the template's shape variation with the shape manifold defined by manually labeled training examples, which makes it possible to separate crowns located in close proximity and prevents the formation of large crown clusters. Also, the statistical information built into the segmentation gives rise to an implicit detection scheme, because the shape template evolves towards an empty contour if not enough evidence for the object is present in the image. We test our method on 3 sample plots from the Bavarian Forest National Park with reference data obtained by manually marking individual dead tree polygons in the images. Our results are scenario-dependent and range from a correctness/completeness of 0.71/0.81 up to 0.77/1, with an average center-of-gravity displacement of 3-5 pixels between the detected and reference polygons.

Multiple Point Statistics algorithm based on direct sampling and multi-resolution images

NASA Astrophysics Data System (ADS)

Julien, S.; Renard, P.; Chugunova, T.

2017-12-01

Multiple Point Statistics (MPS) has become popular for more than one decade in Earth Sciences, because these methods allow to generate random fields reproducing highly complex spatial features given in a conceptual model, the training image, while classical geostatistics techniques based on bi-point statistics (covariance or variogram) fail to generate realistic models. Among MPS methods, the direct sampling consists in borrowing patterns from the training image to populate a simulation grid. This latter is sequentially filled by visiting each of these nodes in a random order, and then the patterns, whose the number of nodes is fixed, become narrower during the simulation process, as the simulation grid is more densely informed. Hence, large scale structures are caught in the beginning of the simulation and small scale ones in the end. However, MPS may mix spatial characteristics distinguishable at different scales in the training image, and then loose the spatial arrangement of different structures. To overcome this limitation, we propose to perform MPS simulation using a decomposition of the training image in a set of images at multiple resolutions. Applying a Gaussian kernel onto the training image (convolution) results in a lower resolution image, and iterating this process, a pyramid of images depicting fewer details at each level is built, as it can be done in image processing for example to lighten the space storage of a photography. The direct sampling is then employed to simulate the lowest resolution level, and then to simulate each level, up to the finest resolution, conditioned to the level one rank coarser. This scheme helps reproduce the spatial structures at any scale of the training image and then generate more realistic models. We illustrate the method with aerial photographies (satellite images) and natural textures. Indeed, these kinds of images often display typical structures at different scales and are well-suited for MPS simulation techniques.

A data colocation grid framework for big data medical image processing: backend design

NASA Astrophysics Data System (ADS)

Bao, Shunxing; Huo, Yuankai; Parvathaneni, Prasanna; Plassard, Andrew J.; Bermudez, Camilo; Yao, Yuang; Lyu, Ilwoo; Gokhale, Aniruddha; Landman, Bennett A.

2018-03-01

When processing large medical imaging studies, adopting high performance grid computing resources rapidly becomes important. We recently presented a "medical image processing-as-a-service" grid framework that offers promise in utilizing the Apache Hadoop ecosystem and HBase for data colocation by moving computation close to medical image storage. However, the framework has not yet proven to be easy to use in a heterogeneous hardware environment. Furthermore, the system has not yet validated when considering variety of multi-level analysis in medical imaging. Our target design criteria are (1) improving the framework's performance in a heterogeneous cluster, (2) performing population based summary statistics on large datasets, and (3) introducing a table design scheme for rapid NoSQL query. In this paper, we present a heuristic backend interface application program interface (API) design for Hadoop and HBase for Medical Image Processing (HadoopBase-MIP). The API includes: Upload, Retrieve, Remove, Load balancer (for heterogeneous cluster) and MapReduce templates. A dataset summary statistic model is discussed and implemented by MapReduce paradigm. We introduce a HBase table scheme for fast data query to better utilize the MapReduce model. Briefly, 5153 T1 images were retrieved from a university secure, shared web database and used to empirically access an in-house grid with 224 heterogeneous CPU cores. Three empirical experiments results are presented and discussed: (1) load balancer wall-time improvement of 1.5-fold compared with a framework with built-in data allocation strategy, (2) a summary statistic model is empirically verified on grid framework and is compared with the cluster when deployed with a standard Sun Grid Engine (SGE), which reduces 8-fold of wall clock time and 14-fold of resource time, and (3) the proposed HBase table scheme improves MapReduce computation with 7 fold reduction of wall time compare with a naïve scheme when datasets are relative small. The source code and interfaces have been made publicly available.

Computer experimental analysis of the CHP performance of a 100 kW e SOFC Field Unit by a factorial design

NASA Astrophysics Data System (ADS)

Calì, M.; Santarelli, M. G. L.; Leone, P.

Gas Turbine Technologies (GTT) and Politecnico di Torino, both located in Torino (Italy), have been involved in the design and installation of a SOFC laboratory in order to analyse the operation, in cogenerative configuration, of the CHP 100 kW e SOFC Field Unit, built by Siemens-Westinghouse Power Corporation (SWPC), which is at present (May 2005) starting its operation and which will supply electric and thermal power to the GTT factory. In order to take the better advantage from the analysis of the on-site operation, and especially to correctly design the scheduled experimental tests on the system, we developed a mathematical model and run a simulated experimental campaign, applying a rigorous statistical approach to the analysis of the results. The aim of this work is the computer experimental analysis, through a statistical methodology (2 k factorial experiments), of the CHP 100 performance. First, the mathematical model has been calibrated with the results acquired during the first CHP100 demonstration at EDB/ELSAM in Westerwoort. After, the simulated tests have been performed in the form of computer experimental session, and the measurement uncertainties have been simulated with perturbation imposed to the model independent variables. The statistical methodology used for the computer experimental analysis is the factorial design (Yates' Technique): using the ANOVA technique the effect of the main independent variables (air utilization factor U ox, fuel utilization factor U F, internal fuel and air preheating and anodic recycling flow rate) has been investigated in a rigorous manner. Analysis accounts for the effects of parameters on stack electric power, thermal recovered power, single cell voltage, cell operative temperature, consumed fuel flow and steam to carbon ratio. Each main effect and interaction effect of parameters is shown with particular attention on generated electric power and stack heat recovered.

A Data Colocation Grid Framework for Big Data Medical Image Processing: Backend Design.

PubMed

Bao, Shunxing; Huo, Yuankai; Parvathaneni, Prasanna; Plassard, Andrew J; Bermudez, Camilo; Yao, Yuang; Lyu, Ilwoo; Gokhale, Aniruddha; Landman, Bennett A

2018-03-01

When processing large medical imaging studies, adopting high performance grid computing resources rapidly becomes important. We recently presented a "medical image processing-as-a-service" grid framework that offers promise in utilizing the Apache Hadoop ecosystem and HBase for data colocation by moving computation close to medical image storage. However, the framework has not yet proven to be easy to use in a heterogeneous hardware environment. Furthermore, the system has not yet validated when considering variety of multi-level analysis in medical imaging. Our target design criteria are (1) improving the framework's performance in a heterogeneous cluster, (2) performing population based summary statistics on large datasets, and (3) introducing a table design scheme for rapid NoSQL query. In this paper, we present a heuristic backend interface application program interface (API) design for Hadoop & HBase for Medical Image Processing (HadoopBase-MIP). The API includes: Upload, Retrieve, Remove, Load balancer (for heterogeneous cluster) and MapReduce templates. A dataset summary statistic model is discussed and implemented by MapReduce paradigm. We introduce a HBase table scheme for fast data query to better utilize the MapReduce model. Briefly, 5153 T1 images were retrieved from a university secure, shared web database and used to empirically access an in-house grid with 224 heterogeneous CPU cores. Three empirical experiments results are presented and discussed: (1) load balancer wall-time improvement of 1.5-fold compared with a framework with built-in data allocation strategy, (2) a summary statistic model is empirically verified on grid framework and is compared with the cluster when deployed with a standard Sun Grid Engine (SGE), which reduces 8-fold of wall clock time and 14-fold of resource time, and (3) the proposed HBase table scheme improves MapReduce computation with 7 fold reduction of wall time compare with a naïve scheme when datasets are relative small. The source code and interfaces have been made publicly available.

A Data Colocation Grid Framework for Big Data Medical Image Processing: Backend Design

PubMed Central

Huo, Yuankai; Parvathaneni, Prasanna; Plassard, Andrew J.; Bermudez, Camilo; Yao, Yuang; Lyu, Ilwoo; Gokhale, Aniruddha; Landman, Bennett A.

2018-01-01

When processing large medical imaging studies, adopting high performance grid computing resources rapidly becomes important. We recently presented a "medical image processing-as-a-service" grid framework that offers promise in utilizing the Apache Hadoop ecosystem and HBase for data colocation by moving computation close to medical image storage. However, the framework has not yet proven to be easy to use in a heterogeneous hardware environment. Furthermore, the system has not yet validated when considering variety of multi-level analysis in medical imaging. Our target design criteria are (1) improving the framework’s performance in a heterogeneous cluster, (2) performing population based summary statistics on large datasets, and (3) introducing a table design scheme for rapid NoSQL query. In this paper, we present a heuristic backend interface application program interface (API) design for Hadoop & HBase for Medical Image Processing (HadoopBase-MIP). The API includes: Upload, Retrieve, Remove, Load balancer (for heterogeneous cluster) and MapReduce templates. A dataset summary statistic model is discussed and implemented by MapReduce paradigm. We introduce a HBase table scheme for fast data query to better utilize the MapReduce model. Briefly, 5153 T1 images were retrieved from a university secure, shared web database and used to empirically access an in-house grid with 224 heterogeneous CPU cores. Three empirical experiments results are presented and discussed: (1) load balancer wall-time improvement of 1.5-fold compared with a framework with built-in data allocation strategy, (2) a summary statistic model is empirically verified on grid framework and is compared with the cluster when deployed with a standard Sun Grid Engine (SGE), which reduces 8-fold of wall clock time and 14-fold of resource time, and (3) the proposed HBase table scheme improves MapReduce computation with 7 fold reduction of wall time compare with a naïve scheme when datasets are relative small. The source code and interfaces have been made publicly available. PMID:29887668

A comparison of dynamical and statistical downscaling methods for regional wave climate projections along French coastlines.

NASA Astrophysics Data System (ADS)

Laugel, Amélie; Menendez, Melisa; Benoit, Michel; Mattarolo, Giovanni; Mendez, Fernando

2013-04-01

Wave climate forecasting is a major issue for numerous marine and coastal related activities, such as offshore industries, flooding risks assessment and wave energy resource evaluation, among others. Generally, there are two main ways to predict the impacts of the climate change on the wave climate at regional scale: the dynamical and the statistical downscaling of GCM (Global Climate Model). In this study, both methods have been applied on the French coast (Atlantic , English Channel and North Sea shoreline) under three climate change scenarios (A1B, A2, B1) simulated with the GCM ARPEGE-CLIMAT, from Météo-France (AR4, IPCC). The aim of the work is to characterise the wave climatology of the 21st century and compare the statistical and dynamical methods pointing out advantages and disadvantages of each approach. The statistical downscaling method proposed by the Environmental Hydraulics Institute of Cantabria (Spain) has been applied (Menendez et al., 2011). At a particular location, the sea-state climate (Predictand Y) is defined as a function, Y=f(X), of several atmospheric circulation patterns (Predictor X). Assuming these climate associations between predictor and predictand are stationary, the statistical approach has been used to project the future wave conditions with reference to the GCM. The statistical relations between predictor and predictand have been established over 31 years, from 1979 to 2009. The predictor is built as the 3-days-averaged squared sea level pressure gradient from the hourly CFSR database (Climate Forecast System Reanalysis, http://cfs.ncep.noaa.gov/cfsr/). The predictand has been extracted from the 31-years hindcast sea-state database ANEMOC-2 performed with the 3G spectral wave model TOMAWAC (Benoit et al., 1996), developed at EDF R&D LNHE and Saint-Venant Laboratory for Hydraulics and forced by the CFSR 10m wind field. Significant wave height, peak period and mean wave direction have been extracted with an hourly-resolution at 110 coastal locations along the French coast. The model, based on the BAJ parameterization of the source terms (Bidlot et al, 2007) was calibrated against ten years of GlobWave altimeter observations (2000-2009) and validated through deep and shallow water buoy observations. The dynamical downscaling method has been performed with the same numerical wave model TOMAWAC used for building ANEMOC-2. Forecast simulations are forced by the 10m wind fields of ARPEGE-CLIMAT (A1B, A2, B1) from 2010 to 2100. The model covers the Atlantic Ocean and uses a spatial resolution along the French and European coast of 10 and 20 km respectively. The results of the model are stored with a time resolution of one hour. References: Benoit M., Marcos F., and F. Becq, (1996). Development of a third generation shallow-water wave model with unstructured spatial meshing. Proc. 25th Int. Conf. on Coastal Eng., (ICCE'1996), Orlando (Florida, USA), pp 465-478. Bidlot J-R, Janssen P. and Adballa S., (2007). A revised formulation of ocean wave dissipation and its model impact, technical memorandum ECMWF n°509. Menendez, M., Mendez, F.J., Izaguirre,C., Camus, P., Espejo, A., Canovas, V., Minguez, R., Losada, I.J., Medina, R. (2011). Statistical Downscaling of Multivariate Wave Climate Using a Weather Type Approach, 12th International Workshop on Wave Hindcasting and Forecasting and 3rd Coastal Hazard Symposium, Kona (Hawaii).

Building environment assessment and energy consumption estimation using smart phones

NASA Astrophysics Data System (ADS)

Li, Xiangli; Zhang, Li; Jia, Yingqi; Wang, Zihan; Jin, Xin; Zhao, Xuefeng

2017-04-01

In this paper, an APP for building indoor environment evaluation and energy consumption estimation based on Android platform is proposed and established. While using the APP, the smart phone built-in sensors are called for real-time monitoring of the building environmental information such as temperature, humidity and noise, etc. the built-in algorithm is developed to calculate the heat and power consumption, and questionnaires, grading and other methods are used to feed back to the space heating system. In addition, with the application of the technology of big data and cloud technology, the data collected by users will be uploaded to the cloud. After the statistics of the uploaded data, regional difference can be obtained, thus providing a more accurate basis for macro-control and research of energy, thermal comfort, greenhouse effect.

Prediction of Chemical Function: Model Development and ...

EPA Pesticide Factsheets

The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi

Statistical machine translation for biomedical text: are we there yet?

PubMed

Wu, Cuijun; Xia, Fei; Deleger, Louise; Solti, Imre

2011-01-01

In our paper we addressed the research question: "Has machine translation achieved sufficiently high quality to translate PubMed titles for patients?". We analyzed statistical machine translation output for six foreign language - English translation pairs (bi-directionally). We built a high performing in-house system and evaluated its output for each translation pair on large scale both with automated BLEU scores and human judgment. In addition to the in-house system, we also evaluated Google Translate's performance specifically within the biomedical domain. We report high performance for German, French and Spanish -- English bi-directional translation pairs for both Google Translate and our system.

tscvh R Package: Computational of the two samples test on microarray-sequencing data

NASA Astrophysics Data System (ADS)

Fajriyah, Rohmatul; Rosadi, Dedi

2017-12-01

We present a new R package, a tscvh (two samples cross-variance homogeneity), as we called it. This package is a software of the cross-variance statistical test which has been proposed and introduced by Fajriyah ([3] and [4]), based on the cross-variance concept. The test can be used as an alternative test for the significance difference between two means when sample size is small, the situation which is usually appeared in the bioinformatics research. Based on its statistical distribution, the p-value can be also provided. The package is built under a homogeneity of variance between samples.

Calculating solar photovoltaic potential on residential rooftops in Kailua Kona, Hawaii

NASA Astrophysics Data System (ADS)

Carl, Caroline

As carbon based fossil fuels become increasingly scarce, renewable energy sources are coming to the forefront of policy discussions around the globe. As a result, the State of Hawaii has implemented aggressive goals to achieve energy independence by 2030. Renewable electricity generation using solar photovoltaic technologies plays an important role in these efforts. This study utilizes geographic information systems (GIS) and Light Detection and Ranging (LiDAR) data with statistical analysis to identify how much solar photovoltaic potential exists for residential rooftops in the town of Kailua Kona on Hawaii Island. This study helps to quantify the magnitude of possible solar photovoltaic (PV) potential for Solar World SW260 monocrystalline panels on residential rooftops within the study area. Three main areas were addressed in the execution of this research: (1) modeling solar radiation, (2) estimating available rooftop area, and (3) calculating PV potential from incoming solar radiation. High resolution LiDAR data and Esri's solar modeling tools and were utilized to calculate incoming solar radiation on a sample set of digitized rooftops. Photovoltaic potential for the sample set was then calculated with the equations developed by Suri et al. (2005). Sample set rooftops were analyzed using a statistical model to identify the correlation between rooftop area and lot size. Least squares multiple linear regression analysis was performed to identify the influence of slope, elevation, rooftop area, and lot size on the modeled PV potential values. The equations built from these statistical analyses of the sample set were applied to the entire study region to calculate total rooftop area and PV potential. The total study area statistical analysis findings estimate photovoltaic electric energy generation potential for rooftops is approximately 190,000,000 kWh annually. This is approximately 17 percent of the total electricity the utility provided to the entire island in 2012. Based on these findings, full rooftop PV installations on the 4,460 study area homes could provide enough energy to power over 31,000 homes annually. The methods developed here suggest a means to calculate rooftop area and PV potential in a region with limited available data. The use of LiDAR point data offers a major opportunity for future research in both automating rooftop inventories and calculating incoming solar radiation and PV potential for homeowners.

Development of a 3D Underground Cadastral System with Indoor Mapping for As-Built BIM: The Case Study of Gangnam Subway Station in Korea

PubMed Central

Kim, Sangmin; Kim, Jeonghyun; Jung, Jaehoon; Heo, Joon

2015-01-01

The cadastral system provides land ownership information by registering and representing land boundaries on a map. The current cadastral system in Korea, however, focuses mainly on the management of 2D land-surface boundaries. It is not yet possible to provide efficient or reliable land administration, as this 2D system cannot support or manage land information on 3D properties (including architectures and civil infrastructures) for both above-ground and underground facilities. A geometrical model of the 3D parcel, therefore, is required for registration of 3D properties. This paper, considering the role of the cadastral system, proposes a framework for a 3D underground cadastral system that can register various types of 3D underground properties using indoor mapping for as-built Building Information Modeling (BIM). The implementation consists of four phases: (1) geometric modeling of a real underground infrastructure using terrestrial laser scanning data; (2) implementation of as-built BIM based on geometric modeling results; (3) accuracy assessment for created as-built BIM using reference points acquired by total station; and (4) creation of three types of 3D underground cadastral map to represent underground properties. The experimental results, based on indoor mapping for as-built BIM, show that the proposed framework for a 3D underground cadastral system is able to register the rights, responsibilities, and restrictions corresponding to the 3D underground properties. In this way, clearly identifying the underground physical situation enables more reliable and effective decision-making in all aspects of the national land administration system. PMID:26690174

Equivalent uniform dose concept evaluated by theoretical dose volume histograms for thoracic irradiation.

PubMed

Dumas, J L; Lorchel, F; Perrot, Y; Aletti, P; Noel, A; Wolf, D; Courvoisier, P; Bosset, J F

2007-03-01

The goal of our study was to quantify the limits of the EUD models for use in score functions in inverse planning software, and for clinical application. We focused on oesophagus cancer irradiation. Our evaluation was based on theoretical dose volume histograms (DVH), and we analyzed them using volumetric and linear quadratic EUD models, average and maximum dose concepts, the linear quadratic model and the differential area between each DVH. We evaluated our models using theoretical and more complex DVHs for the above regions of interest. We studied three types of DVH for the target volume: the first followed the ICRU dose homogeneity recommendations; the second was built out of the first requirements and the same average dose was built in for all cases; the third was truncated by a small dose hole. We also built theoretical DVHs for the organs at risk, in order to evaluate the limits of, and the ways to use both EUD(1) and EUD/LQ models, comparing them to the traditional ways of scoring a treatment plan. For each volume of interest we built theoretical treatment plans with differences in the fractionation. We concluded that both volumetric and linear quadratic EUDs should be used. Volumetric EUD(1) takes into account neither hot-cold spot compensation nor the differences in fractionation, but it is more sensitive to the increase of the irradiated volume. With linear quadratic EUD/LQ, a volumetric analysis of fractionation variation effort can be performed.

The Built Environment and Active Travel: Evidence from Nanjing, China

PubMed Central

Feng, Jianxi

2016-01-01

Background: An established relationship exists between the built environment and active travel. Nevertheless, the literature examining the impacts of different components of the built environment is limited. In addition, most existing studies are based on data from cities in the U.S. and Western Europe. The situation in Chinese cities remains largely unknown. Based on data from Nanjing, China, this study explicitly examines the influences of two components of the built environment—the neighborhood form and street form—on residents’ active travel. Methods: Binary logistic regression analyses examined the effects of the neighborhood form and street form on subsistence, maintenance and discretionary travel, respectively. For each travel purpose, three models are explored: a model with only socio-demographics, a model with variables of the neighborhood form and a complete model with all variables. Results: The model fit indicator, Nagelkerke’s ρ2, increased by 0.024 when neighborhood form variables are included and increased by 0.070 when street form variables are taken into account. A similar situation can be found in the models of maintenance activities and discretionary activities. Regarding specific variables, very limited significant impacts of the neighborhood form variables are observed, while almost all of the characteristics of the street form show significant influences on active transport. Conclusions: In Nanjing, street form factors have a more profound influence on active travel than neighborhood form factors. The focal point of the land use regulations and policy of local governments should shift from the neighborhood form to the street form to maximize the effects of policy interventions. PMID:27005645

Nonlinear analyses and failure patterns of typical masonry school buildings in the epicentral zone of the 2016 Italian earthquakes

NASA Astrophysics Data System (ADS)

Clementi, Cristhian; Clementi, Francesco; Lenci, Stefano

2017-11-01

The paper discusses the behavior of typical masonry school buildings in the center of Italy built at the end of 1950s without any seismic guidelines. These structures have faced the recent Italian earthquakes in 2016 without diffuse damages. Global numerical models of the building have been built and masonry material has been simulated as nonlinear. Sensitivity analyses are done to evaluate the reliability of the structural models.

Re-sampling remotely sensed data to improve national and regional mapping of forest conditions with confidential field data

Treesearch

Raymond L. Czaplewski

2005-01-01

Forest Service Research and Development (R&D) and State and Private Forestry Deputy Areas, in partnership with the National Forest System Remote Sensing Applications Center (RSAC), built a 250-m resolution (6.25-ha pixel) dataset for the entire USA. It assembles multi-seasonal hyperspectral MODIS data and derivatives, Landsat derivatives (i.e., summary statistics...

AVOCADO: A Virtual Observatory Census to Address Dwarfs Origins

NASA Astrophysics Data System (ADS)

Sánchez-Janssen, Rubén; Sánchez-Janssen

2011-12-01

Dwarf galaxies are by far the most abundant of all galaxy types, yet their properties are still poorly understood-especially due to the observational challenge that their intrinsic faintness represents. AVOCADO aims at establishing firm conclusions on their formation and evolution by constructing a homogeneous, multiwavelength dataset for a statistically significant sample of several thousand nearby dwarfs (-18 < Mi < -14). Using public data and Virtual Observatory tools, we have built GALEX+SDSS+2MASS spectral energy distributions that are fitted by a library of single stellar population models. Star formation rates, stellar masses, ages and metallicities are further complemented with structural parameters that can be used to classify them morphologically. This unique dataset, coupled with a detailed characterization of each dwarf's environment, allows for a fully comprehensive investigation of their origins and to track the (potential) evolutionary paths between the different dwarf types.

Towards real-time metabolic profiling of a biopsy specimen during a surgical operation by 1H high resolution magic angle spinning nuclear magnetic resonance: a case report.

PubMed

Piotto, Martial; Moussallieh, François-Marie; Neuville, Agnès; Bellocq, Jean-Pierre; Elbayed, Karim; Namer, Izzie Jacques

2012-01-18

Providing information on cancerous tissue samples during a surgical operation can help surgeons delineate the limits of a tumoral invasion more reliably. Here, we describe the use of metabolic profiling of a colon biopsy specimen by high resolution magic angle spinning nuclear magnetic resonance spectroscopy to evaluate tumoral invasion during a simulated surgical operation. Biopsy specimens (n = 9) originating from the excised right colon of a 66-year-old Caucasian women with an adenocarcinoma were automatically analyzed using a previously built statistical model. Metabolic profiling results were in full agreement with those of a histopathological analysis. The time-response of the technique is sufficiently fast for it to be used effectively during a real operation (17 min/sample). Metabolic profiling has the potential to become a method to rapidly characterize cancerous biopsies in the operation theater.

[Specificity of the Adultrap for capturing females of Aedes aegypti (Diptera: Culicidae)].

PubMed

Gomes, Almério de Castro; da Silva, Nilza Nunes; Bernal, Regina Tomie Ivata; Leandro, André de Souza; de Camargo, Natal Jataí; da Silva, Allan Martins; Ferreira, Adão Celestino; Ogura, Luis Carlos; de Oliveira, Sebastião José; de Moura, Silvestre Marques

2007-01-01

The Adultrap is a new trap built for capturing females of Aedes aegypti. Tests were carried out to evaluate the specificity of this trap in comparison with the technique of aspiration of specimens in artificial shelters. Adultraps were kept for 24 hours inside and outside 120 randomly selected homes in two districts of the city of Foz do Iguaçú, State of Paraná. The statistical test was Poissons log-linear model. The result was 726 mosquitoes captured, of which 80 were Aedes aegypti. The Adultrap captured only females of this species, while the aspiration method captured both sexes of Aedes aegypti and another five species. The Adultrap captured Aedes aegypti inside and outside the homes, but the analysis indicated that, outside the homes, this trap captured significantly more females than aspiration did. The sensitivity of the Adultrap for detecting females of Aedes aegypti in low-frequency situations was also demonstrated.

A conceptual prediction model for seasonal drought processes using atmospheric and oceanic standardized anomalies: application to regional drought processes in China

NASA Astrophysics Data System (ADS)

Liu, Zhenchen; Lu, Guihua; He, Hai; Wu, Zhiyong; He, Jian

2018-01-01

Reliable drought prediction is fundamental for water resource managers to develop and implement drought mitigation measures. Considering that drought development is closely related to the spatial-temporal evolution of large-scale circulation patterns, we developed a conceptual prediction model of seasonal drought processes based on atmospheric and oceanic standardized anomalies (SAs). Empirical orthogonal function (EOF) analysis is first applied to drought-related SAs at 200 and 500 hPa geopotential height (HGT) and sea surface temperature (SST). Subsequently, SA-based predictors are built based on the spatial pattern of the first EOF modes. This drought prediction model is essentially the synchronous statistical relationship between 90-day-accumulated atmospheric-oceanic SA-based predictors and SPI3 (3-month standardized precipitation index), calibrated using a simple stepwise regression method. Predictor computation is based on forecast atmospheric-oceanic products retrieved from the NCEP Climate Forecast System Version 2 (CFSv2), indicating the lead time of the model depends on that of CFSv2. The model can make seamless drought predictions for operational use after a year-to-year calibration. Model application to four recent severe regional drought processes in China indicates its good performance in predicting seasonal drought development, despite its weakness in predicting drought severity. Overall, the model can be a worthy reference for seasonal water resource management in China.

Forward Modeling of Large-scale Structure: An Open-source Approach with Halotools

NASA Astrophysics Data System (ADS)

Hearin, Andrew P.; Campbell, Duncan; Tollerud, Erik; Behroozi, Peter; Diemer, Benedikt; Goldbaum, Nathan J.; Jennings, Elise; Leauthaud, Alexie; Mao, Yao-Yuan; More, Surhud; Parejko, John; Sinha, Manodeep; Sipöcz, Brigitta; Zentner, Andrew

2017-11-01

We present the first stable release of Halotools (v0.2), a community-driven Python package designed to build and test models of the galaxy-halo connection. Halotools provides a modular platform for creating mock universes of galaxies starting from a catalog of dark matter halos obtained from a cosmological simulation. The package supports many of the common forms used to describe galaxy-halo models: the halo occupation distribution, the conditional luminosity function, abundance matching, and alternatives to these models that include effects such as environmental quenching or variable galaxy assembly bias. Satellite galaxies can be modeled to live in subhalos or to follow custom number density profiles within their halos, including spatial and/or velocity bias with respect to the dark matter profile. The package has an optimized toolkit to make mock observations on a synthetic galaxy population—including galaxy clustering, galaxy-galaxy lensing, galaxy group identification, RSD multipoles, void statistics, pairwise velocities and others—allowing direct comparison to observations. Halotools is object-oriented, enabling complex models to be built from a set of simple, interchangeable components, including those of your own creation. Halotools has an automated testing suite and is exhaustively documented on http://halotools.readthedocs.io, which includes quickstart guides, source code notes and a large collection of tutorials. The documentation is effectively an online textbook on how to build and study empirical models of galaxy formation with Python.

Does the social environment moderate associations of the built environment with Latinas' objectively-measured neighborhood outdoor physical activity?

PubMed

Perez, L G; Carlson, J; Slymen, D J; Patrick, K; Kerr, J; Godbole, S; Elder, J P; Ayala, G X; Arredondo, E M

2016-12-01

Favorable perceptions of the built and social neighborhood environment may promote outdoor physical activity (PA). However, little is known about their independent and interactive effects on neighborhood-specific outdoor PA. We examined associations of perceived built and social neighborhood environment factors, and their interactions, with objectively-measured neighborhood outdoor moderate-to-vigorous physical activity (MVPA) among a sample of Latina women in San Diego, CA. Analyses included baseline data collected in 2011-2013 from 86 Latinas with ≥ 2 days of combined accelerometer and global positioning system data and complete survey measures. We examined objective neighborhood outdoor MVPA within 500-meter home buffers. Generalized linear mixed models examined associations of 3 perceived built (e.g., sidewalk maintenance) and 3 social environmental (e.g., safety from crime) factors with engaging in any daily neighborhood outdoor MVPA. Models tested interactions between the built and social environmental factors. Although the perceived neighborhood environmental factors were not significantly related to daily neighborhood outdoor MVPA, we found 2 significant interactions: perceived sidewalk maintenance x safety from crime ( p  = 0.05) and neighborhood aesthetics x neighborhood social cohesion ( p  = 0.03). Sidewalk maintenance was positively related to daily neighborhood outdoor MVPA only among Latinas that reported low levels of safety from crime. Neighborhood aesthetics was positively related to daily neighborhood outdoor MVPA only among Latinas with high neighborhood social cohesion. Findings suggest several built and social environmental factors interact to influence Latinas' neighborhood outdoor MVPA. Interventions are needed targeting both built and social neighborhood environmental factors favorable to outdoor PA in the neighborhood.

Complexity in built environment, health, and destination walking: a neighborhood-scale analysis.

PubMed

Carlson, Cynthia; Aytur, Semra; Gardner, Kevin; Rogers, Shannon

2012-04-01

This study investigates the relationships between the built environment, the physical attributes of the neighborhood, and the residents' perceptions of those attributes. It focuses on destination walking and self-reported health, and does so at the neighborhood scale. The built environment, in particular sidewalks, road connectivity, and proximity of local destinations, correlates with destination walking, and similarly destination walking correlates with physical health. It was found, however, that the built environment and health metrics may not be simply, directly correlated but rather may be correlated through a series of feedback loops that may regulate risk in different ways in different contexts. In particular, evidence for a feedback loop between physical health and destination walking is observed, as well as separate feedback loops between destination walking and objective metrics of the built environment, and destination walking and perception of the built environment. These feedback loops affect the ability to observe how the built environment correlates with residents' physical health. Previous studies have investigated pieces of these associations, but are potentially missing the more complex relationships present. This study proposes a conceptual model describing complex feedback relationships between destination walking and public health, with the built environment expected to increase or decrease the strength of the feedback loop. Evidence supporting these feedback relationships is presented.

The Spatial Variations and Hotspots of Ecosystem Service Values in India During 1985 - 2005

NASA Astrophysics Data System (ADS)

Sannigrahi, S.; Bhatt, S.; Paul, S. K.; Sen, S.; Rahmat, S.; Uniyal, B.

2017-12-01

Ecosystem services can be defined as the processes and functions of the natural ecosystem through which the basic needs and demands of human well-being are sustained and fulfilled. In this study, the spatiotemporal variation and clusters of ESV are analyzed during 1985 - 2005 using benefits transfer approach. The spatial heterogeneity of ESV was assessed through Local Morans I, Getis-Ord-Gi hotspot, and Geary's I statistics, respectively. The temporal uncertainty of ESV was estimated through Ecosystem Service Change Index (ESCI), Ecosystem Service Status Index (ESSI). The sensitivity of ESV to land use/land cover changes were estimated using the Coefficient of Elasticity (CE) approach. The variability of ESV in different land-use categories was examined through Coefficient of Variation (CV), Theil Index (TI), Ginni Coefficient and Entropy method. The contribution of ESV to GDP was quantified using the percent of ESV (%ESV) approach. Total eight ecoregions were considered for estimating the changing dynamics of high and low ESV clusters during 1985 - 2005. Among the eight ecoregions, grassland shows the minimum CV of ESV (1.15) during 1985 - 2005, indicating the most stable and undisturbed eco-regions in India during this study period, followed by cropland (CV= 2.0), forest land (2.32), water bodies (3.27), mangrove (3.5) respectively. While, the urban built-up (CV = 15.89) and wetland (CV = 10.91) exhibits the highest CV during 1985 - 2005. The ESCI was found highly negative (-0.05) over forested region, and negative (-0.02) for water bodies, while a very highly positive (0.38), highly positive (0.24), moderate positive (0.06), weakly positive (0.03) and very weak positive (0.02) values have quantified for urban built-up, wetland, mangrove, cropland and, grassland, respectively. The overall ESSI was very good for urban built-up categories (0.24), followed by wetland (0.15), mangrove (0.04), cropland (0.02) and, grassland (0.01), respectively. During this period (1985 - 2005), the changes of ESV were found statistically significant for urban built-up (t = 10.90, P = 0.008), and wetland (t = 15.88, P = 0.004), while the changes were insignificant for rest of the ecoregions.

The evolving cobweb of relations among partially rational investors

PubMed Central

DiMeglio, Anna; Garofalo, Franco; Lo Iudice, Francesco

2017-01-01

To overcome the limitations of neoclassical economics, researchers have leveraged tools of statistical physics to build novel theories. The idea was to elucidate the macroscopic features of financial markets from the interaction of its microscopic constituents, the investors. In this framework, the model of the financial agents has been kept separate from that of their interaction. Here, instead, we explore the possibility of letting the interaction topology emerge from the model of the agents’ behavior. Then, we investigate how the emerging cobweb of relationship affects the overall market dynamics. To this aim, we leverage tools from complex systems analysis and nonlinear dynamics, and model the network of mutual influence as the output of a dynamical system describing the edge evolution. In this work, the driver of the link evolution is the relative reputation between possibly coupled agents. The reputation is built differently depending on the extent of rationality of the investors. The continuous edge activation or deactivation induces the emergence of leaders and of peculiar network structures, typical of real influence networks. The subsequent impact on the market dynamics is investigated through extensive numerical simulations in selected scenarios populated by partially rational investors. PMID:28196144

The Problem of Size in Robust Design

NASA Technical Reports Server (NTRS)

Koch, Patrick N.; Allen, Janet K.; Mistree, Farrokh; Mavris, Dimitri

1997-01-01

To facilitate the effective solution of multidisciplinary, multiobjective complex design problems, a departure from the traditional parametric design analysis and single objective optimization approaches is necessary in the preliminary stages of design. A necessary tradeoff becomes one of efficiency vs. accuracy as approximate models are sought to allow fast analysis and effective exploration of a preliminary design space. In this paper we apply a general robust design approach for efficient and comprehensive preliminary design to a large complex system: a high speed civil transport (HSCT) aircraft. Specifically, we investigate the HSCT wing configuration design, incorporating life cycle economic uncertainties to identify economically robust solutions. The approach is built on the foundation of statistical experimentation and modeling techniques and robust design principles, and is specialized through incorporation of the compromise Decision Support Problem for multiobjective design. For large problems however, as in the HSCT example, this robust design approach developed for efficient and comprehensive design breaks down with the problem of size - combinatorial explosion in experimentation and model building with number of variables -and both efficiency and accuracy are sacrificed. Our focus in this paper is on identifying and discussing the implications and open issues associated with the problem of size for the preliminary design of large complex systems.

An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modelling.

PubMed

Mansouri, K; Grulke, C M; Richard, A M; Judson, R S; Williams, A J

2016-11-01

The increasing availability of large collections of chemical structures and associated experimental data provides an opportunity to build robust QSAR models for applications in different fields. One common concern is the quality of both the chemical structure information and associated experimental data. Here we describe the development of an automated KNIME workflow to curate and correct errors in the structure and identity of chemicals using the publicly available PHYSPROP physicochemical properties and environmental fate datasets. The workflow first assembles structure-identity pairs using up to four provided chemical identifiers, including chemical name, CASRNs, SMILES, and MolBlock. Problems detected included errors and mismatches in chemical structure formats, identifiers and various structure validation issues, including hypervalency and stereochemistry descriptions. Subsequently, a machine learning procedure was applied to evaluate the impact of this curation process. The performance of QSAR models built on only the highest-quality subset of the original dataset was compared with the larger curated and corrected dataset. The latter showed statistically improved predictive performance. The final workflow was used to curate the full list of PHYSPROP datasets, and is being made publicly available for further usage and integration by the scientific community.

The evolving cobweb of relations among partially rational investors.

PubMed

DeLellis, Pietro; DiMeglio, Anna; Garofalo, Franco; Lo Iudice, Francesco

2017-01-01

To overcome the limitations of neoclassical economics, researchers have leveraged tools of statistical physics to build novel theories. The idea was to elucidate the macroscopic features of financial markets from the interaction of its microscopic constituents, the investors. In this framework, the model of the financial agents has been kept separate from that of their interaction. Here, instead, we explore the possibility of letting the interaction topology emerge from the model of the agents' behavior. Then, we investigate how the emerging cobweb of relationship affects the overall market dynamics. To this aim, we leverage tools from complex systems analysis and nonlinear dynamics, and model the network of mutual influence as the output of a dynamical system describing the edge evolution. In this work, the driver of the link evolution is the relative reputation between possibly coupled agents. The reputation is built differently depending on the extent of rationality of the investors. The continuous edge activation or deactivation induces the emergence of leaders and of peculiar network structures, typical of real influence networks. The subsequent impact on the market dynamics is investigated through extensive numerical simulations in selected scenarios populated by partially rational investors.

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

Assessing the impact of energy efficiency technologies at a district or city scale is of great interest to local governments, real estate developers, utility companies, and policymakers. This paper describes a flexible framework that can be used to create and run district and city scale building energy simulations. The framework is built around the new OpenStudio City Database (CityDB). Building footprints, building height, building type, and other data can be imported from public records or other sources. Missing data can be inferred or assigned from a statistical sampling of other datasets. Once all required data is available, OpenStudio Measures aremore » used to create starting point energy models and to model energy efficiency measures for each building. Together this framework allows a user to pose several scenarios such as 'what if 30% of the commercial retail buildings added rooftop solar' or 'what if all elementary schools converted to ground source heat pumps' and then visualize the impacts at a district or city scale. This paper focuses on modeling existing building stock using public records. However, the framework is capable of supporting the evaluation of new construction, district systems, and the use of proprietary data sources.« less

On the Empirical Importance of the Conditional Skewness Assumption in Modelling the Relationship between Risk and Return

NASA Astrophysics Data System (ADS)

Pipień, M.

2008-09-01

We present the results of an application of Bayesian inference in testing the relation between risk and return on the financial instruments. On the basis of the Intertemporal Capital Asset Pricing Model, proposed by Merton we built a general sampling distribution suitable in analysing this relationship. The most important feature of our assumptions is that the skewness of the conditional distribution of returns is used as an alternative source of relation between risk and return. This general specification relates to Skewed Generalized Autoregressive Conditionally Heteroscedastic-in-Mean model. In order to make conditional distribution of financial returns skewed we considered the unified approach based on the inverse probability integral transformation. In particular, we applied hidden truncation mechanism, inverse scale factors, order statistics concept, Beta and Bernstein distribution transformations and also a constructive method. Based on the daily excess returns on the Warsaw Stock Exchange Index we checked the empirical importance of the conditional skewness assumption on the relation between risk and return on the Warsaw Stock Market. We present posterior probabilities of all competing specifications as well as the posterior analysis of the positive sign of the tested relationship.

Antecedents of obesity - analysis, interpretation, and use of longitudinal data.

PubMed

Gillman, Matthew W; Kleinman, Ken

2007-07-01

The obesity epidemic causes misery and death. Most epidemiologists accept the hypothesis that characteristics of the early stages of human development have lifelong influences on obesity-related health outcomes. Unfortunately, there is a dearth of data of sufficient scope and individual history to help unravel the associations of prenatal, postnatal, and childhood factors with adult obesity and health outcomes. Here the authors discuss analytic methods, the interpretation of models, and the use to which such rare and valuable data may be put in developing interventions to combat the epidemic. For example, analytic methods such as quantile and multinomial logistic regression can describe the effects on body mass index range rather than just its mean; structural equation models may allow comparison of the contributions of different factors at different periods in the life course. Interpretation of the data and model construction is complex, and it requires careful consideration of the biologic plausibility and statistical interpretation of putative causal factors. The goals of discovering modifiable determinants of obesity during the prenatal, postnatal, and childhood periods must be kept in sight, and analyses should be built to facilitate them. Ultimately, interventions in these factors may help prevent obesity-related adverse health outcomes for future generations.

Evaluating the effectiveness of Behavior-Based Safety education methods for commercial vehicle drivers.

PubMed

Wang, Xuesong; Xing, Yilun; Luo, Lian; Yu, Rongjie

2018-08-01

Risky driving behavior is one of the main causes of commercial vehicle related crashes. In order to achieve safer vehicle operation, safety education for drivers is often provided. However, the education programs vary in quality and may not always be successful in reducing crash rates. Behavior-Based Safety (BBS) education is a popular approach found effective by numerous studies, but even this approach varies as to the combination of frequency, mode and content used by different education providers. This study therefore evaluates and compares the effectiveness of BBS education methods. Thirty-five drivers in Shanghai, China, were coached with one of three different BBS education methods for 13 weeks following a 13-week baseline phase with no education. A random-effects negative binomial (NB) model was built and calibrated to investigate the relationship between BBS education and the driver at-fault safety-related event rate. Based on the results of the random-effects NB model, event modification factors (EMF) were calculated to evaluate and compare the effectiveness of the methods. Results show that (1) BBS education was confirmed to be effective in safety-related event reduction; (2) the most effective method among the three applied monthly face-to-face coaching, including feedback with video and statistical data, and training on strategies to avoid driver-specific unsafe behaviors; (3) weekly telephone coaching using statistics and strategies was rated by drivers as the most convenient delivery mode, and was also significantly effective. Copyright © 2018 Elsevier Ltd. All rights reserved.

Control of surface thermal scratch of strip in tandem cold rolling

NASA Astrophysics Data System (ADS)

Chen, Jinshan; Li, Changsheng

2014-07-01

The thermal scratch seriously affects the surface quality of the cold rolled stainless steel strip. Some researchers have carried out qualitative and theoretical studies in this field. However, there is currently a lack of research on effective forecast and control of thermal scratch defects in practical production, especially in tandem cold rolling. In order to establish precise mathematical model of oil film thickness in deformation zone, the lubrication in cold rolling process of SUS410L stainless steel strip is studied, and major factors affecting oil film thickness are also analyzed. According to the principle of statistics, mathematical model of critical oil film thickness in deformation zone for thermal scratch is built, with fitting and regression analytical method, and then based on temperature comparison method, the criterion for deciding thermal scratch defects is put forward. Storing and calling data through SQL Server 2010, a software on thermal scratch defects control is developed through Microsoft Visual Studio 2008 by MFC technique for stainless steel in tandem cold rolling, and then it is put into practical production. Statistics indicate that the hit rate of thermal scratch is as high as 92.38%, and the occurrence rate of thermal scratch is decreased by 89.13%. Owing to the application of the software, the rolling speed is increased by approximately 9.3%. The software developed provides an effective solution to the problem of thermal scratch defects in tandem cold rolling, and helps to promote products surface quality of stainless steel strips in practical production.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lian, Jun, E-mail: jun-lian@med.unc.edu; Chera, Bhishamjit S.; Chang, Sha

Purpose: To build a statistical model to quantitatively correlate the anatomic features of structures and the corresponding dose-volume histogram (DVH) of head and neck (HN) Tomotherapy (Tomo) plans. To study if the model built upon one intensity modulated radiation therapy (IMRT) technique (such as conventional Linac) can be used to predict anticipated organs-at-risk (OAR) DVH of patients treated with a different IMRT technique (such as Tomo). To study if the model built upon the clinical experience of one institution can be used to aid IMRT planning for another institution. Methods: Forty-four Tomotherapy intensity modulate radiotherapy plans of HN cases (Tomo-IMRT)more » from Institution A were included in the study. A different patient group of 53 HN fixed gantry IMRT (FG-IMRT) plans was selected from Institution B. The analyzed OARs included the parotid, larynx, spinal cord, brainstem, and submandibular gland. Two major groups of anatomical features were considered: the volumetric information and the spatial information. The volume information includes the volume of target, OAR, and overlapped volume between target and OAR. The spatial information of OARs relative to PTVs was represented by the distance-to-target histogram (DTH). Important anatomical and dosimetric features were extracted from DTH and DVH by principal component analysis. Two regression models, one for Tomotherapy plan and one for IMRT plan, were built independently. The accuracy of intratreatment-modality model prediction was validated by a leave one out cross-validation method. The intertechnique and interinstitution validations were performed by using the FG-IMRT model to predict the OAR dosimetry of Tomo-IMRT plans. The dosimetry of OARs, under the same and different institutional preferences, was analyzed to examine the correlation between the model prediction and planning protocol. Results: Significant patient anatomical factors contributing to OAR dose sparing in HN Tomotherapy plans have been analyzed and identified. For all the OARs, the discrepancies of dose indices between the model predicted values and the actual plan values were within 2.1%. Similar results were obtained from the modeling of FG-IMRT plans. The parotid gland was spared in a comparable fashion during the treatment planning of two institutions. The model based on FG-IMRT plans was found to predict the median dose of the parotid of Tomotherapy plans quite well, with a mean error of 2.6%. Predictions from the FG-IMRT model suggested the median dose of the larynx, median dose of the brainstem and D2 of the brainstem could be reduced by 10.5%, 12.8%, and 20.4%, respectively, in the Tomo-IMRT plans. This was found to be correlated to the institutional differences in OAR constraint settings. Re-planning of six Tomotherapy patients confirmed the potential of optimization improvement predicted by the FG-IMRT model was correct. Conclusions: The authors established a mathematical model to correlate the anatomical features and dosimetric indexes of OARs of HN patients in Tomotherapy plans. The model can be used for the setup of patient-specific OAR dose sparing goals and quality control of planning results. The institutional clinical experience was incorporated into the model which allows the model from one institution to generate a reference plan for another institution, or another IMRT technique.« less

Prediction of body mass index status from voice signals based on machine learning for automated medical applications.

PubMed

Lee, Bum Ju; Kim, Keun Ho; Ku, Boncho; Jang, Jun-Su; Kim, Jong Yeol

2013-05-01

The body mass index (BMI) provides essential medical information related to body weight for the treatment and prognosis prediction of diseases such as cardiovascular disease, diabetes, and stroke. We propose a method for the prediction of normal, overweight, and obese classes based only on the combination of voice features that are associated with BMI status, independently of weight and height measurements. A total of 1568 subjects were divided into 4 groups according to age and gender differences. We performed statistical analyses by analysis of variance (ANOVA) and Scheffe test to find significant features in each group. We predicted BMI status (normal, overweight, and obese) by a logistic regression algorithm and two ensemble classification algorithms (bagging and random forests) based on statistically significant features. In the Female-2030 group (females aged 20-40 years), classification experiments using an imbalanced (original) data set gave area under the receiver operating characteristic curve (AUC) values of 0.569-0.731 by logistic regression, whereas experiments using a balanced data set gave AUC values of 0.893-0.994 by random forests. AUC values in Female-4050 (females aged 41-60 years), Male-2030 (males aged 20-40 years), and Male-4050 (males aged 41-60 years) groups by logistic regression in imbalanced data were 0.585-0.654, 0.581-0.614, and 0.557-0.653, respectively. AUC values in Female-4050, Male-2030, and Male-4050 groups in balanced data were 0.629-0.893 by bagging, 0.707-0.916 by random forests, and 0.695-0.854 by bagging, respectively. In each group, we found discriminatory features showing statistical differences among normal, overweight, and obese classes. The results showed that the classification models built by logistic regression in imbalanced data were better than those built by the other two algorithms, and significant features differed according to age and gender groups. Our results could support the development of BMI diagnosis tools for real-time monitoring; such tools are considered helpful in improving automated BMI status diagnosis in remote healthcare or telemedicine and are expected to have applications in forensic and medical science. Copyright © 2013 Elsevier B.V. All rights reserved.